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

1.5.3 Face Alignment Using Interacting Snakes
For the semantic recognition system (shown in Fig. 1.12), we define a semantic face
graph. A semantic face graph is derived from a generic 3D face model for identifying
faces at the semantic level. The nodes of a semantic graph represent high-level facial
components (e.g., eyes and mouth), whose boundaries are described by open (or
closed) active contours (or snakes). In our recognition system, face alignment plays
a crucial role in adapting a priori knowledge of facial topology, encoded in semantic
face graph, onto the sensed facial measurements (e.g., face images). The semantic
face graph is first projected onto a 2D image, coarsely aligned to the output of the
face detection module, and then finely adapted to the face images using interacting
snakes.
Snakes are useful models for extracting the shape of deformable objects [77].
Hence, we model the component boundaries of a 2D semantic face graph as a collection
of snakes. We propose an approach for manipulating multiple snakes iteratively,
called interacting snakes, that minimizes the attraction energy functionals on both
contours and enclosed regions of individual snakes and the repulsion energy functionals
among multiple snakes that interact with each other. We evaluate the interacting
snakes through two types of implementations, explicit (parametric active contours)
and implicit (geodesic active contours) curve representations, for face alignment.
Once the semantic face graph has been aligned to face images, we can derive
component weights based on distinctiveness and visibility of individual components.
The aligned face graph can also be easily used to generate cartoon faces and facial
25