Facial Recognition

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Facial Recognition

• CSE 391
• Kris Lord

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Background

• 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

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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)

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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

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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

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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.

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Success rate?

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

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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
  future
• Online dating profiling

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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