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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