Roweisposes, Including Eigenposes, Supervised Eigenposes, and Fisherposes, for 3D Action Recognition

TL;DR

This paper introduces Roweisposes, a family of generalized subspace learning methods based on Roweis discriminant analysis, for improved 3D human action recognition, unifying several existing pose-based approaches.

Contribution

It proposes a novel framework called Roweisposes that generalizes eigenposes and Fisherposes using Roweis discriminant analysis for 3D action recognition.

Findings

Effective on TST, UTKinect, and UCFKinect datasets

Unifies multiple pose recognition methods

Demonstrates improved recognition accuracy

Abstract

Human action recognition is one of the important fields of computer vision and machine learning. Although various methods have been proposed for 3D action recognition, some of which are basic and some use deep learning, the need of basic methods based on generalized eigenvalue problem is sensed for action recognition. This need is especially sensed because of having similar basic methods in the field of face recognition such as eigenfaces and Fisherfaces. In this paper, we propose Roweisposes which uses Roweis discriminant analysis for generalized subspace learning. This method includes Fisherposes, eigenposes, supervised eigenposes, and double supervised eigenposes as its special cases. Roweisposes is a family of infinite number of action recongition methods which learn a discriminative subspace for embedding the body poses. Experiments on the TST, UTKinect, and UCFKinect datasets verify the effectiveness of the proposed method for action recognition.