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Face Recognition

Face Recognition

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Face Recognition

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  1. Face Recognition

  2. Introduction • Why we are interested in face recognition? • Passport control at terminals in airports • Participant identification in meetings • System access control • Scanning for criminal persons

  3. Face Recognition • Face is the most common biometric used by humans • Applications range from static, mug-shot verification to a dynamic, uncontrolled face identification in a cluttered background • Challenges: • automatically locate the face • recognize the face from a general view point under different illumination conditions, facial expressions, and aging effects

  4. Authentication vs Identification • Face Authentication/Verification (1:1 matching) • Face Identification/recognition(1:n matching)

  5. Applications • It is not fool proof – many have been fooled by identical twins • Because of these, use of facial biometrics for identification is often questioned.

  6. Application • Video Surveillance (On-line or off-line) • locates and extracts images from video footage for identification and verification

  7. Why is Face Recognition Hard? • Many faces of Madonna

  8. Why is Face Recognition Hard?

  9. Face Recognition Difficulties • Identify similar faces (inter-class similarity) • Accommodate intra-class variability due to: • head pose • illumination conditions • expressions • facial accessories • aging effects • Cartoon faces

  10. Inter-class Similarity • Different persons may have very similar appearance Twins Father and son

  11. Intra-class Variability • Faces with intra-subject variations in pose, illumination, expression, accessories, color, occlusions, and brightness

  12. Sketch of a Pattern RecognitionArchitecture

  13. Example: Face Detection • Scan window over image. • Classify window as either: • Face • Non-face

  14. Profile views • Schneiderman’s Test set as an example

  15. Example: Finding skinNon-parametric Representation of CCD • Skin has a very small range of (intensity independent) colors, and little texture • Compute an intensity-independent color measure, check if color is in this range, check if there is little texture (median filter) • See this as a classifier - we can set up the tests by hand, or learn them. • get class conditional densities (histograms), priors from data (counting) • Classifier is

  16. Face Detection Algorithm

  17. Face Recognition

  18. Face Recognition: 2-D and 3-D

  19. Image as a Feature Vector • Consider an n-pixel image to be a point in an n-dimensional space, • Each pixel value is a coordinate of x.

  20. Nearest Neighbor Classifier • Rj is the training dataset • The match for I is R1, who is closer than R2

  21. Comments • Sometimes called “Template Matching” • Variations on distance function • Multiple templates per class- perhaps many training images per class. • Expensive to compute k distances, especially when each image is big (N dimensional). • May not generalize well to unseen examples of class. • Some solutions: • Bayesian classification • Dimensionality reduction

  22. Face Recognition Solutions • Holistic or Appearance-based Face recognition • EigenFace • LDA • Feature-based

  23. EigenFace

  24. EigenFace • Use Principle Component Analysis (PCA) to determine the most discriminating features between images of faces. • The principal component analysis or Karhunen-Loeve transform is a mathematical way of determining that linear transformation of a sample of points in L-dimensional space which exhibits the properties of the sample most clearly along the coordinate axes.

  25. PCA •

  26. More New Techniques in Face Biometrics • Facial geometry, 3D face recognition 3D reconstruction

  27. Skin pattern recognition • using the details of the skin for authentication

  28. Facial thermogram • Facial thermogram requires an (expensive) infrared camera to detect the facial heat patterns that are unique to every human being. Technology Recognition Systems worked on that subject in 1996-1999. Now disappeared.

  29. Side effect of Facial thermogram • can detect lies • The image on the left shows his normal facial thermogram, and the image on the right shows the temperature changes when he lied.

  30. Smile recognition • Probing the characteristic pattern of muscles beneath the skin of the face. • Analyzing how the skin around the subject's mouth moves between the two smiles. • Tracking changes in the position of tiny wrinkles in the skin, each just a fraction of a millimetre wide. • The data is used to produce an image of the face overlaid with tiny arrows that indicate how different areas of skin move during a smile.

  31. Dynamic facial features • They track the motion of certain features on the face during a facial expression (e.g., smile) and obtain a vector field that characterizes the deformation of the face.