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Face Recognition University of Calgary PowerPoint Presentation

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On : Jan 08, 2015

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  • Slide 1 - Face Recognition CPSC 601 Biometric Course
  • Slide 2 - Topics Challenges in face recognition Face detection Face recognition Advantages and disadvantages
  • Slide 3 - Face Recognition
  • Slide 4 - Issues in human face recognition Face recognition appears to be a dedicated process of the brain Holistic and feature information are used in the recognition process Face memory is highly viewpoint-dependent Analysis of facial expressions is accomplished in parallel to face recognition Humans recognize people from their own race better than people from another race
  • Slide 5 - Face Recognition Probably the most common biometric characteristic used by humans Non-intrusive technique which people generally accept as a biometric characteristic Dependent on imaging/devices Overt (user aware) and covert (user unaware) applications Subject of intensive research for over 25 years Challenges Physical appearance Acquisition geometry Imaging conditions Compression artifacts
  • Slide 6 - Challenges
  • Slide 7 - Imaging
  • Slide 8 - Face Detection Face detection task: to identify and locate human faces in an image regardless of their position, scale, in plane rotation, orientation, pose (out of plane rotation), and illumination. The first step for any automatic face recognition system Face detection methods: Knowledge-based Feature invariant approaches Template matching methods Appearance-based methods Representation: How to describe a typical face?
  • Slide 9 - Face Detection Methods Knowledge-based methods Encode human knowledge of what constitutes a typical face (usually, the relationship between facial features) Feature invariant approaches Aim to find structural features of a face that exist even when the pose, viewpoint, or lighting conditions vary Template matching methods Several standard patterns stored to describe the face as a whole or the facial features separately Appearance-based methods The models (or templates) are learned from a set of training images which capture the representative variability of facial appearance
  • Slide 10 - Knowledge-based method Multi-resolution focus-of-attention approach Level 1: (lowest resolution): apply the rule “the center part of the face has 4 cells with a basically uniform intensity” to search for candidates search for candidates Level 2: local histogram equalization followed by edge detection Level 3: search for eye and mouth features for validation
  • Slide 11 - Feature-invariant approach
  • Slide 12 - Template matching method Several standard patterns stored to describe the face as a whole or the facial features separately
  • Slide 13 - Appearance-Based Methods The models (or templates) are learned from a set of training images which capture the representative variability of facial appearance. Method is based on recognizing specific facial manifolds using Principal component analysis.
  • Slide 14 - 14 Color-based scheme Skin color Filtering: Human skin has its own color distribution that differs from that of most of nonface objects. It can be used to filter the input image to obtain candidate regions of faces, and also to construct a stand-alone skin color-based face detector for special environments. A skin color likelihood model, p(color|face), can be derived from skin color samples. Skin color filtering. Input image (left) and skin color-filtered map (right).
  • Slide 15 - Face Recognition In general, face recognition systems proceed by detecting the face in the scene, thus estimating and normalizing for translation, scale, and in-plane rotation. Many approaches to finding faces are based on weak models of the human face that model face shape in terms of facial texture. Once a prospective face has been localized, the approaches to face recognition then divided into two categories: Face appearance Face geometry
  • Slide 16 - Detection and Localization
  • Slide 17 - Examples of Detections
  • Slide 18 - Face Recognition Methods The underlying idea behind these approaches is to reduce a facial image containing thousands of pixels before making comparisons To do this, a face image is transformed into a space that is spanned by basis image functions, just like a Fourier transform projects an image onto basis images of the fundamental frequencies.
  • Slide 19 - Face Recognition Methods Direct Correlation Function-based (Principal Component Analysis, Fisher-based Descriminant method) Geometry-based methods (elastic graph matching, triangulation, face geoemtry
  • Slide 20 - Correlation Two images are superimposed and the correlation between corresponding pixels is computed for different alignments.
  • Slide 21 - Principal Component Analysis (PCA) Principal component analysis (PCA), or Karhunen-Loeve transformation, is a data-reduction method that finds an alternative set of parameters for a set of raw data (or features) such that most of the variability in the data is compressed down to the first few parameters The transformed PCA parameters are orthogonal The PCA diagonalizes the covariance matrix, and the resulting diagonal elements are the variances of the transformed PCA parameters
  • Slide 22 - PCA A face image defines a point in the high-dimensional image space Different face images share a number of similarities with each other They can be described by a relatively low-dimensional subspace They can be projected into an appropriately chosen subspace of eigenfaces and classification can be performed by similarity computation (distance)
  • Slide 23 - Principal Component Analysis (PCA)
  • Slide 24 - Elastic Graph Matching Each face is represented by a set of feature vectors positioned on the nodes of a coarse 2D grid placed on the face Each feature vector is comprised of a set of responses of 2D Gabor wavelets, differing in orientation and scale Comparing two faces is accomplished by matching and adapting the grid of a test image to the grid of a reference image, where both grids have the same number of nodes; the test grid has initially the same structure as the reference grid. The elasticity of the test grid allows accommodation of face distortions (e.g., due to the expression change) and to a lesser extent, changes in the view point. The quality of match is evaluated using a distance function
  • Slide 25 - Elastic Graph Matching
  • Slide 26 - 3D Face
  • Slide 27 - 3D Face
  • Slide 28 - Face geometry Here the idea is to model a human face in terms of particular face features, such as eyes, mouth, etc., and the geometry of the layout of these features. Face recognition is then a matter of matching feature constellations
  • Slide 29 - Face Recognition: Advantages Photos of faces are widely used in passports and driver’s licenses where the possession authentication protocol is augmented with a photo for manual inspection purposes; there is wide public acceptance for this biometric identifier Face recognition systems are the least intrusive from a biometric sampling point of view, requiring no contact, nor even the awareness of the subject The biometric works, or at least works in theory, with legacy photograph data-bases, videotape, or other image sources Face recognition can, at least in theory, be used for screening of unwanted individuals in a crowd, in real time It is a fairly good biometric identifier for small-scale verification applications
  • Slide 30 - Face Recognition: Disadvantages A face needs to be well lighted by controlled light sources in automated face authentication systems. This is only a first challenge in a long list of technical challenges that are associated with robust face authentication Face currently is a poor biometric for use in a pure identification protocol An obvious circumvention method is disguise There is some criminal association with face identifiers since this biometric has long been used by law enforcement agencies (‘mugshots’).
  • Slide 31 - Reference and Links Signal Processing Institute, Swiss Federal Institute of Technology http://scgwww.epfl.ch/ Biometric Systems Lab, University of Bologna http://bias.csr.unibo.it/research/biolab/

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