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Facial Recognition


Difficulties arise due to large variation in facial appearance, head size, ... Online dating profiling. Current State of the Art. Neural Net algorithms ... – PowerPoint PPT presentation

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Title: Facial Recognition

Facial Recognition
  • CSE 391
  • Kris Lord

  • Face recognition is one of the fundamental
    problems in pattern analysis
  • Difficulties arise due to large variation in
    facial appearance, head size, orientation and
    change in environmental conditions
  • Computerized face recognition system still cannot
    achieve a completely reliable performance

Main Issues
  • Often in practical situations, recognition must
    be achieved in real-time so efficiency and speed
    are crucial
  • Variance in lighting, angles, and other
    environmental areas make recognition more of a
    problem to deal with
  • May be hard to obtain a complete database of a
    populations faces in optimal posture/lighting
    for processing
  • False positives/inability to recognize a face
    still common in current state of algorithms
  • Storage space a large issue, especially when
    dealing with matrix-based algorithms (the more
    detailed the picture, the larger the storage
    space needed)

3 Main Steps
  • Face detection
  • - Facial area is singled out and removed
  • for processing within a noisy image
  • Face normalization
  • - Facial image is processed to counteract
    posture issues such as tilt, angle, lighting, and
    other environmental noise
  • Face verification/recognition
  • - Facial features are analyzed via a
    recognition algorithm to determine a match with
    an existing face in a database

Eigenfaces Approach
  • Patterns, in the domain of facial recognition
    could be the presence of some objects (eyes,
    nose, mouth) in a face as well as relative
    distances between these objects. These
    characteristic features are called eigenfaces in
    the facial recognition domain (or principal
    components generally). They can be extracted out
    of original image data by means of a mathematical
    tool called Principal Component Analysis (PCA).
  • Each eigenface represents only certain features
    of the face. If the feature is present in the
    original image to a higher degree, the share of
    the corresponding eigenface in the sum of the
    eigenfaces should be greater.
  • In order to cut down on large computational
    processing, only eigenfaces with the highest
    value (most characteristic facial features) are
    kept for processing

Common Eigenface Algorithm
  • A set of training data (pictures of faces) are
    transformed into a set E of Eigenfaces
  • Afterwards, the weights are calculated for each
    image of the training set and stored in the set W
  • Upon observing an unknown image X, the weights
    are calculated for that particular image and
    stored in the vector WX. Afterwards, WX is
    compared with the weights of images, of which one
    knows for certain that they are faces (the
    weights of the training set W)
  • If this average distance exceeds some threshold
    value , then the weight vector of the unknown
    image WX lies too far apart from the weights of
    the faces. In this case, the unknown X is
    considered to not a face. If it is considered to
    be a face, its weight vector WX is stored for
    later classification, where it can be tested
    against specific images and their eigenfaces.

Success rate?
  • Some algorithms are much more successful than
  • Success rate depends greatly on database of faces
  • Rate can vary considerably if databases are
    combined (eigenface success rate drops
    considerably, to 66 with combined databases)

Practical Applications
  • Combat Terrorism/Airport Security
  • Large event (e.g. Superbowl) security ability
    to scan the crowd with a video camera and match
    against a database of criminal records
  • Eliminate fake IDs
  • Eliminate identity theft (ATMs)
  • Casino security
  • Tailored (personalized) advertisements of the
  • Online dating profiling

Current State of the Art
  • Neural Net algorithms
  • Elastic matching algorithms
  • NEC Developed 3D face recognition algorithm with
    over 96.5 recognition rate under bad
    environmental conditions
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