X

Download Face Recognition Chinese PowerPoint Presentation

SlidesFinder-Advertising-Design.jpg

Login   OR  Register
X


Iframe embed code :



Presentation url :

Home / Science & Technology / Science & Technology Presentations / Face Recognition Chinese PowerPoint Presentation

Face Recognition Chinese PowerPoint Presentation

Ppt Presentation Embed Code   Zoom Ppt Presentation

PowerPoint is the world's most popular presentation software which can let you create professional Face Recognition Chinese powerpoint presentation easily and in no time. This helps you give your presentation on Face Recognition Chinese in a conference, a school lecture, a business proposal, in a webinar and business and professional representations.

The uploader spent his/her valuable time to create this Face Recognition Chinese powerpoint presentation slides, to share his/her useful content with the world. This ppt presentation uploaded by worldwideweb in Science & Technology ppt presentation category is available for free download,and can be used according to your industries like finance, marketing, education, health and many more.

About This Presentation

Slide 1 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP
Slide 2 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session
Slide 3 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only
Slide 4 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition
Slide 5 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet
Slide 6 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model.
Slide 7 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background
Slide 8 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network.
Slide 9 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image
Slide 10 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow
Slide 11 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation.
Slide 12 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains
Slide 13 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image
Slide 14 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way.
Slide 15 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part
Slide 16 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance
Slide 17 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet
Slide 18 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance
Slide 19 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector
Slide 20 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C
Slide 21 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image
Slide 22 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold
Slide 23 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold Eigenface (cont’) Advantages Fast on Recognition Easy to implement Disadvantages Finding the eigenvectors and eigenvalues are time consuming on PPC The size and location of each face image must remain similar
Slide 24 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold Eigenface (cont’) Advantages Fast on Recognition Easy to implement Disadvantages Finding the eigenvectors and eigenvalues are time consuming on PPC The size and location of each face image must remain similar Template-based Method The most direct method used for face recognition is the matching between the test images and a set of training images based on measuring the correlation. The similarity is obtained by normalize cross correlation.
Slide 25 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold Eigenface (cont’) Advantages Fast on Recognition Easy to implement Disadvantages Finding the eigenvectors and eigenvalues are time consuming on PPC The size and location of each face image must remain similar Template-based Method The most direct method used for face recognition is the matching between the test images and a set of training images based on measuring the correlation. The similarity is obtained by normalize cross correlation. Template-based Method (cont’) Advantages: Easy to implement Disadvantages: Highly sensitive to illumination Not reliable Expensive computation in order to achieve scale invariance.
Slide 26 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold Eigenface (cont’) Advantages Fast on Recognition Easy to implement Disadvantages Finding the eigenvectors and eigenvalues are time consuming on PPC The size and location of each face image must remain similar Template-based Method The most direct method used for face recognition is the matching between the test images and a set of training images based on measuring the correlation. The similarity is obtained by normalize cross correlation. Template-based Method (cont’) Advantages: Easy to implement Disadvantages: Highly sensitive to illumination Not reliable Expensive computation in order to achieve scale invariance. Gabor Wavelet Gabor wavelet can be used to extract the information of face. Matching with the feature extracted by Gabor wavelet Advantages and Disadvantages are the same as that of Face Detection.
Slide 27 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold Eigenface (cont’) Advantages Fast on Recognition Easy to implement Disadvantages Finding the eigenvectors and eigenvalues are time consuming on PPC The size and location of each face image must remain similar Template-based Method The most direct method used for face recognition is the matching between the test images and a set of training images based on measuring the correlation. The similarity is obtained by normalize cross correlation. Template-based Method (cont’) Advantages: Easy to implement Disadvantages: Highly sensitive to illumination Not reliable Expensive computation in order to achieve scale invariance. Gabor Wavelet Gabor wavelet can be used to extract the information of face. Matching with the feature extracted by Gabor wavelet Advantages and Disadvantages are the same as that of Face Detection. Conclusion Limitations need to be considered Computational power of PPC Time constraint of the project Methods used in our project Gabor wavelet is used in face detection EigenFace is used in face recognition Both are fast and not difficult to implement
Slide 28 - Smart Traveller with Visual Translator for OCR and Face Recognition LYU0203 FYP Outline Introduction Face Detection Face Recognition Methods for Face Detection Methods for Face Recognition Conclusion Q&A session Introduction Our FYP project consists of two parts – Korean OCR and Face Recognition Today, we present the issues of face recognition only Introduction (cont’) Face Detection Find Face Region Facial Feature Face Recognition Identify the person Input Image Face Region/ position of facial feature Person’s name Framework of Face recognition Methods for Face Detection Color-based model Neural Network Coarse to fine method Gabor wavelet Color Based Model We can find the face region by color. YUV or YIQ color model is usually used in color classification. Usually face color is within a small space in color model. Mathematical equations are used to represent face color in these color model. Color Model (cont’) Advantages: Easy to implement Fast Disadvantages: Not reliable (especially photo taken by camera in PPC) Affected by complex background Neural Network It is a pure pattern recognition. (no color information needed) In principal, the popular back-propagation neural network can be trained to detect face images directly. The intensity of the image is the input of the neural network. Neural Network (cont’) The procedure is similar to the algorithm proposed by CMU Manually collect large amount of face image (about 1000) The image is scaled to 20x20 pixels. Create non-face image with random pixel intensities. Train the neural network to produce 1 for face image and -1 for non-face image Neural Network (cont’) Advantages: High accuracy (detection rate ~90%) Not difficult to implement Disadvantages: Difficult to train Slow Coarse-to-fine method Hierarchical architecture is used to find the facial feature. Position, scale and orientation are partitioned into a sequence of nested partitions with different constraint. A set of edge detectors is used to find the range of position, scale and orientation. Coarse-to-fine method (cont’) Partition with loose constrains Partition with strict constrains Coarse-to-fine method (cont’) Advantages: Fast Acceptable accuracy with simple background Disadvantages: High resolution image is required Fail to find face with blurred image Gabor Wavelet A simple model for the responses of simple cells in the primary visual cortex. It extracts edge and shape information. It can represent face image in a very compact way. Gabor Wavelet (cont’) Real Part Imaginary Part Gabor Wavelet (cont’) Advantages: Fast Acceptable accuracy Small training set Disadvantages: Affected by complex background Slightly rotation invariance Methods for Face Recognition EigenFace Template-based Matching Gabor wavelet EigenFace EigenFace is a common method for face recognition Principal Component Analysis (PCA) is used Find the covariance of the training images Compute the eigenvectors of the covariance EigenFace (cont’) Procedure Scale the face images into 20x20 pixels size Each face image is a 400-dimensional vector Find the average face by where M is the number of the face images and T is the face images vector EigenFace (cont’) Procedure (cont’) Find the Covariance Matrix by where Compute the eigenvectors and eigenvalues of C EigenFace (cont’) Procedure (cont’) The M’ significant eigenvectors are chosen as those with the largest corresponding eigenvalues Project all the face images into these eigenvectors and form the feature vectors of each face image EigenFace (cont’) Procedure (cont’) For recognition Project the test face image to the eigenvectors Find the difference (Euclidean Distance) between the projected vector and each face image feature vector Choose the minimum one as the result or reject all if the differences are greater than a threshold Eigenface (cont’) Advantages Fast on Recognition Easy to implement Disadvantages Finding the eigenvectors and eigenvalues are time consuming on PPC The size and location of each face image must remain similar Template-based Method The most direct method used for face recognition is the matching between the test images and a set of training images based on measuring the correlation. The similarity is obtained by normalize cross correlation. Template-based Method (cont’) Advantages: Easy to implement Disadvantages: Highly sensitive to illumination Not reliable Expensive computation in order to achieve scale invariance. Gabor Wavelet Gabor wavelet can be used to extract the information of face. Matching with the feature extracted by Gabor wavelet Advantages and Disadvantages are the same as that of Face Detection. Conclusion Limitations need to be considered Computational power of PPC Time constraint of the project Methods used in our project Gabor wavelet is used in face detection EigenFace is used in face recognition Both are fast and not difficult to implement Q&A Session
 

Frequently Asked Questions

> About SlidesFinder?

Slidesfinder is a website that allows users to upload and share their PowerPoint presentations online. It provides a platform for individuals and businesses to share their ideas and knowledge through presentations, and for others to view and download these presentations for educational or professional purposes.
Slidesfinder offers a wide range of presentation topics and categories, including business, education, science, technology, health, and more. Users can also search for presentations using keywords, tags, or by category. The website provides tools to create professional presentations with rich media, animations, and graphics.



One of the key features of Slidesfinder is its user-friendly interface, which allows users to easily upload, view, and share presentations. Users can also embed their presentations in their websites or blogs using the website's embed code feature.
Slidesfinder also offers premium services, such as the ability to create private presentations and to download presentations in high-quality formats. However, the basic features of Slidesfinder are free to use, making it accessible to anyone who wants to share their ideas and knowledge through presentations.

> How do I register with SlidesFinder?

Go to registration page (you can see signup link on top of website page) https://www.slidesfinder.com/signup . If you have facebook/gmail account them just click on SIGN IN WITH FACEBOOK OR SIGN IN WITH GOOGLE button, by this you will be a registered member of slidesfinder without filling any form, required detail automatically will be fatch from your account. If you do not a Facebook account, then click on "Signup". Fill all required fields and you will be a registered member of slidesfinder.

> Is slidesfinder account confirmation is mandatory?

Yes it is mandetory to active your account to login.

> Do I need to signup/login on SlidesFinder before uploading a PowerPoint presentation?

Yes, you need to login with your account before uploading presentation. Your username will be displayed on your uploaded presentation. Your registered email id is needed for sending your stats of uploaded presentation.

Slidesfinder is a sharing website for PowerPoint presentations search and share. Find your interest in the form of powerpoint presentations on slidesfinder and save your valuable time . On Slidesfinder you get presentations from our huge library of professional ppt presentations. We believe in making your search INFORMATIVE and FUN. Find your best ppt presentation from a pool of PowerPoint presentations stacked under important industry categories like business & management, heath & Wellness,eduction & training etc. We provide unique informative PowerPoint presentation for marketers, presenters and educationists. These professional PowerPoint presentations are uploaded by professionals from across numerous industry segments.These ppt presentations are available for FREE download.

Not just finding your interest, but facilitate you broadcast your interest. We have created this platform for easy sharing of PowerPoint presentations, ensuring that these presentations get maximum exposure. Create your slidesfinder account and upload PowerPoint presentations for free, share on social media platforms and BUILD YOUR CROWD WITH PRESENTATION !!

The Great Buddha says, "Share your knowledge.It’s a way to achieve immortality"! So, start sharing knowledge and we are here to make that immortal !!

© 2013-2022 SlidesFinder. All rights reserved.

Re-productivity of content are not allowed. Without prior written permission from author, commercial use of any content is illegal.