Title: View-Invariant Recognition of Action Style Self-Dissimilarity,
Abstract: Self-similarity was recently introduced as a measure of inter-class
congruence for classification of actions. Herein, we investigate the dual
problem of intra-class dissimilarity for classification of action styles. We
introduce self-dissimilarity matrices that discriminate between same actions
performed by different subjects regardless of viewing direction and camera
parameters. We investigate two frameworks using these invariant style
dissimilarity measures based on Principal Component Analysis (PCA) and Fisher
Discriminant Analysis (FDA). Extensive experiments performed on IXMAS dataset
indicate remarkably good discriminant characteristics for the proposed
invariant measures for gender recognition from video data.