Face Recognition

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Face Recognition
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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

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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

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Authentication vs Identification

• Face Authentication/Verification (11 matching)

• Face Identification/recognition(11 matching)

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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.

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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
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Why is Face Recognition Hard?

• Many faces of Madonna

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Why is Face Recognition Hard?
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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

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Inter-class Similarity

• Different persons may have very similar appearance

Twins
Father and son
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Intra-class Variability

• Faces with intra-subject variations in pose,
  illumination, expression, accessories, color,
  occlusions, and brightness

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Sketch of a Pattern RecognitionArchitecture
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Example Face Detection

• Scan window over image.
• Classify window as either
• Face
• Non-face

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Profile views

• Schneidermans Test set as an example

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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

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Face Detection Algorithm
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Face Recognition
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Face Recognition 2-D and 3-D
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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.

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Nearest Neighbor Classifier

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

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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

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Face Recognition Solutions

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

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EigenFace
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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.

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PCA

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

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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
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Skin pattern recognition

• using the details of the skin for authentication

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