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IntroductionIntroduction
Why we are interested in face Why we are interested in face recognition?recognition?Passport control at terminals in Passport control at terminals in
airportsairportsParticipant identification in Participant identification in
meetingsmeetingsSystem access controlSystem access controlScanning for criminal personsScanning for criminal persons
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
Authentication vs Identification
Face Authentication/Verification (1:1 matching)
Face Identification/recognition(1:n matching)
Applications
It is not fool proof – many have been It is not fool proof – many have been fooled by identical twinsfooled by identical twins
Because of these, use of facial biometrics Because of these, use of facial biometrics for identification is often questioned.for identification is often questioned.
ApplicationApplication Video Surveillance (On-line or off-line) http://www.crossmatch.com/facesnap-fotoshot.phphttp://www.crossmatch.com/facesnap-fotoshot.php
locates and extracts images from video footage for identification and verification
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
Intra-class Variability Faces with intra-subject variations in pose,
illumination, expression, accessories, color, occlusions, and brightness
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
Consider an n-pixel image to be a point in an n-dimensional space,
Each pixel value is a coordinate of x.
Image as a Feature Vector
nRx
Nearest Neighbor Classifier
Rj is the training datasetRj is the training dataset The match for I is R1, who is closer than R2The match for I is R1, who is closer than R2
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
Holistic or Appearance-based Face Holistic or Appearance-based Face recognitionrecognition EigenFaceEigenFace LDALDA
Feature-basedFeature-based
Face Recognition Face Recognition SolutionsSolutions
EigenFaceEigenFace Use Principle Component Analysis
(PCA) to determine the most discriminating features between images of faces.
The principal component analysis or The principal component analysis or Karhunen-Loeve transformKarhunen-Loeve transform is a is a mathematical way of determining that mathematical way of determining that linear transformation of a sample of linear transformation of a sample of points in points in LL-dimensional space which -dimensional space which exhibits the properties of the sample exhibits the properties of the sample most clearly along the coordinate axes.most clearly along the coordinate axes.
PCAPCA
http://www.cs.otago.ac.nz/cosc453/http://www.cs.otago.ac.nz/cosc453/student_tutorials/student_tutorials/principal_components.pdf principal_components.pdf
More New Techniques in More New Techniques in Face BiometricsFace Biometrics
Facial geometry, 3D face recognitionFacial geometry, 3D face recognition
http://www-users.cs.york.ac.uk/~nep/research/3Dface/tomh/3DFaceDatabase.html
3D reconstruction
Skin pattern recognitionSkin pattern recognition
using the details of the skin for using the details of the skin for authentication authentication
http://pagesperso-orange.fr/http://pagesperso-orange.fr/fingerchip/biometrics/types/face.htmfingerchip/biometrics/types/face.htm
Facial thermogramFacial thermogram Facial thermogram requires an (expensive) Facial thermogram requires an (expensive)
infrared camera to detect the facial heat infrared camera to detect the facial heat patterns that are unique to every human patterns that are unique to every human being. Technology Recognition Systems being. Technology Recognition Systems worked on that subject in 1996-1999. Now worked on that subject in 1996-1999. Now disappeared.disappeared.
http://pagesperso-orange.fr/http://pagesperso-orange.fr/fingerchip/biometrics/types/face.htmfingerchip/biometrics/types/face.htm
Side effect of Facial Side effect of Facial thermogramthermogram
can detect liescan detect lies The image on the left shows his normal The image on the left shows his normal
facial thermogram, and the image on the facial thermogram, and the image on the right shows the temperature changes right shows the temperature changes when he lied. when he lied.
http://pagesperso-orange.fr/http://pagesperso-orange.fr/fingerchip/biometrics/types/face.htmfingerchip/biometrics/types/face.htm
Smile recognitionSmile recognition Probing the characteristic pattern of muscles Probing the characteristic pattern of muscles
beneath the skin of the face.beneath the skin of the face. Analyzing how the skin around the subject's Analyzing how the skin around the subject's
mouth moves between the two smiles. mouth moves between the two smiles. Tracking changes in the position of tiny wrinkles Tracking changes in the position of tiny wrinkles
in the skin, each just a fraction of a millimetre in the skin, each just a fraction of a millimetre wide. wide.
The data is used to produce an image of the face The data is used to produce an image of the face overlaid with tiny arrows that indicate how overlaid with tiny arrows that indicate how different areas of skin move during a smile. different areas of skin move during a smile.
http://pagesperso-orange.fr/http://pagesperso-orange.fr/fingerchip/biometrics/types/face.htmfingerchip/biometrics/types/face.htm
Dynamic facial featuresDynamic facial features
They track the motion of certain features They track the motion of certain features on the face during a facial expression on the face during a facial expression (e.g., smile) and obtain a vector field that (e.g., smile) and obtain a vector field that characterizes the deformation of the face. characterizes the deformation of the face.
http://pagesperso-orange.fr/http://pagesperso-orange.fr/fingerchip/biometrics/types/face.htmfingerchip/biometrics/types/face.htm