Face Detection and Modeling for Recognition - Biometrics Research ...

5.6 Summary
For overcoming variations in pose, illumination, and expression, we propose semantic
face graphs that are extracted from a subset of vertices of a 3D face model, and aligned
to an image for face recognition. We have presented a framework for semantic face
recognition, which is designed to automatically derive weights for facial components
based on their distinctiveness and visibility, and to perform face matching based on
visible facial components.
Face alignment is a crucial module for face matching,
and we implement it in a coarse-to-fine fashion. We have shown examples of coarse
alignment, and have investigated two deformation approaches for fine alignment of
semantic face graphs using interacting snakes.
Experimental results show that a
successful interaction among multiple snakes associated with facial components makes
the semantic face graph a useful model to represent faces (e.g., cartoon faces and
caricatures) for recognition.
Our automatic scheme for aligning faces uses interacting snakes for various facial
components, including the hair outline, face outline, eyes, nose, and mouth.
We
are currently adding snakes for eyebrows to completely automate the whole process
of face alignment. We plan to test the proposed semantic face matching algorithm
on standard face databases. We also plan to implement a pose estimation module
based on the alignment results in order to construct an automated pose-invariant face
recognition system.
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