Multiview Gait Recognition Based on Patch Distribution Features and Uncorrelated Multilinear Sparse Local Discriminant Canonical Correlation Analysis

It is well recognized that gait is an important biometric feature to identify a person at a distance, such as in video surveillance application. However, in reality, a change of viewing angle causes a significant challenge for gait recognition. In this paper, a novel approach is proposed for multiview gait recognition with the view angle of a probe gait sequence unknown. We formulate a new patch distribution feature based classification framework to estimate the view angle of each probe gait sequence. In this method, each gait energy image is represented as a set of dual-tree complex wavelet transform (DTCWT) features derived from different scales and orientations together with the x-y coordinates. Then, a two-stage Gaussian mixture model is presented that can represent each DTCWT based gait feature with a set of patch distribution parameters. A simple nearest-neighbor classifier is employed for view classification. To measure the similarity of gait sequences, we also propose a sparse local discriminant canonical correlation analysis algorithm to model the correlation of gait features from different views and use the correlation strength as similarity measure. An uncorrelated multilinear SLDCCA (UMSLDCCA) framework is further presented that aims to extract uncorrelated discriminative features directly from multidimensional gait features through solving a tensor-to-vector projection. The solution consists of sequential iterative processes based on the alternating projection method. Different from existing approaches, UMSLDCCA considers the spatial structure information within each gait sample and local geometry information among multiple gait samples. Moreover, our approach does not need explicit reconstruction and is robust against feature noise. Extensive experiments have been performed on two benchmark gait databases. The results demonstrate that our method outperforms the state-of-the-art methods in terms of accuracy and efficiency.