Face Recognition by Elastic Bunch Graph Matching

1
Face Recognition by Elastic Bunch Graph Matching

IEEE Trans. PAMI, July 1997
2
(No Transcript)
3
(No Transcript)
4
Gabor Transform

• Gabor Function

Daugman, IEEE Trans. ASSP July 1988
5
Gabor Wavelet Transform

• An implementation of Gabor transform
• Gaussian envelop width ? 2?
• Last term in complex sinusoids removes DC in the
  kernel
• 5 level spatial frequency from 4 to 16 pixels in
  an 128 x 128 image, 8 orientations

Daugman, IEEE Trans. ASSP July 1988
6
Jet

• a set of 40 (5 spatial frequency, 8 orientations)
  complex Gabor wavelet coefficients for one image
  point.
• J a1, a2, , a40
• Similarity between jets
• d is the displacement of pixels needs to be
  estimated.
• kj spatial wave vector

Fig. 1. Similarities Sa(J,J) (dashed line) and
S?(J,J) (solid line) with J taken from the left
eye of a face, and J taken from pixel positions
of the same horizontal line. The dotted line
shows the estimated displacement d (divided by
eight to fit the ordinate range). The right eye
is 24 pixels away from the left eye, generating a
local maximum for both similarity functions and
zero displacement close to dx -24.
7
Face Graph

• Facial fiducial points
• Pupil, tip of mouth, etc.
• Face graph
• Nodes at fiducial pts.
• Un-directed graph
• Object-adaptive
• The structure of graph is the same for each face
• Fitting a face image to a face graph is done
  automatically
• Some nodes may be undefined due to occlusion.
  Hence, association of nodes of different face
  graphs may need to be done manually.

• Bunch
• A set of Jets all asso with the same fiducial pt.
• e.g. an eye Jet may consists of different types
  of eyes open, closed, male, female, etc.
• Face bunch graph (FBG)
• Same as a face graph, except each node consists
  of a jet bunch rather than a jet

8
Face Bunch Graph

• Has the same structure as individual face graph
• Each node labeled with a bunch of jets
• Each edge labeled with average distance between
  corresponding nodes in face samples
• Given a new face, an elastic bunch graph matching
  (EBGM) method selects the best fitting jets
  (local experts) from the bunch dedicated to each
  node in the face bunch graph.

9
Elastic Bunch Graph Matching

• Graph similarity measure
• ? weighting factor

• Initially, manually generate a few FGs to create
  a FBG
• Heuristic algorithm to find the image graph that
  maximizes the similarity
• Coarse scan of image using jets to detect face
• Varying sizes and aspect ratio of FBG to adapt to
  right format of face.
• Finally, all nodes are moved locally to maximize
  SB.

10
Results