Learning Discriminant Person-Specific Facial Models Using Expandable Graphs

Abstract

In this paper, a novel algorithm for finding discriminant person-specific facial models is proposed and tested for frontal face verification. The most discriminant features of a person's face are found and a deformable model is placed in the spatial coordinates that correspond to these discriminant features. The discriminant deformable models, for verifying the person's identity, that are learned through this procedure are elastic graphs that are dense in the facial areas considered discriminant for a specific person and sparse in other less significant facial areas. The discriminant graphs are enhanced by a discriminant feature selection method for the graph nodes in order to find the most discriminant jet features. The proposed approach significantly enhances the performance of elastic graph matching in frontal face verification