Gait Recognition Through MPCA Plus LDA

Abstract

This paper solves the gait recognition problem in a multilinear principal component analysis (MPCA) framework. Gait sequences are naturally described as tensor objects and feature extraction for tensor objects is important in computer vision and pattern recognition applications. Classical principal component analysis (PCA) operates on vectors and it is not directly applicable to gait sequences. This work introduces an MPCA framework for feature extraction from gait sequences by seeking a multilinear projection onto a tensor subspace of lower dimensionality which captures most of the variance of the original gait samples. A subset of the extracted eigen-tensors are selected and the classical LDA is then applied. In experiments, gait recognition results are reported on the Gait Challenge data sets using the proposed solution. The results indicate that with a simple design, the proposed algorithm outperforms the state-of-the-art algorithms.