# View-Invariant Recognition of Action Style Self-Dissimilarity

## TL;DR

This paper introduces view-invariant self-dissimilarity matrices for classifying action styles, demonstrating their effectiveness in gender recognition across different viewpoints using PCA and FDA frameworks.

## Contribution

It presents a novel approach to intra-class dissimilarity for action style classification that is invariant to view and camera parameters.

## Key findings

- Effective discrimination of action styles across viewpoints.
- High accuracy in gender recognition from video data.
- Invariant measures outperform non-invariant methods.

## 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.