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Face Recognition

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Because of these, use of facial biometrics for identification is often questioned. ... Facial geometry, 3D face recognition ... Dynamic facial features ... – PowerPoint PPT presentation

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Title: Face Recognition


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 (11 matching)
  • Face Identification/recognition(11 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)
  • http//www.crossmatch.com/facial_recognition_solut
    ions.html

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
  • Schneidermans 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
(No Transcript)
17
Face Detection Algorithm
18
Face Recognition
19
Face Recognition 2-D and 3-D
20
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.

21
Nearest Neighbor Classifier
  • Rj is the training dataset
  • The match for I is R1, who is closer than R2

22
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

23

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

24
EigenFace
25
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.

26
PCA
  • http//www.cs.otago.ac.nz/cosc453/student_tutorial
    s/principal_components.pdf

27
More New Techniques in Face Biometrics
  • Facial geometry, 3D face recognition

http//www-users.cs.york.ac.uk/nep/research/3Dfac
e/tomh/3DFaceDatabase.html 3D reconstruction
28
Skin pattern recognition
  • using the details of the skin for authentication

http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
29
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.

http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
30
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.

http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
31
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.

http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
32
Dynamic facial features
  • hey 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.

http//pagesperso-orange.fr/fingerchip/biometrics/
types/face.htm
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