Cross View Gait Recognition Using Correlation Strength

Among various factors that can affect the performance of gait recognition, changes in viewpoint pose the biggest problem. In this work, we develop a novel approach to cross-view gait recognition with the view angle of a probe gait sequence unknown. We formulate a Gaussian Process (GP) classification framework to estimate the view angle of each probe gait sequence. To measure the similarity of gait sequences captured at different view angles, we model the correlation of gait sequences from different views using Canonical Correlation Analysis (CCA) and use the correlation strength as similarity measure. This differs significantly from existing approaches, which reconstruct gait features in different views either through 2D view transformation or 3D calibration. Without explicit reconstruction, our approach can cope with feature mis-match across view and is more robust against feature noise. Our experiments validate that the proposed method significantly outperforms the existing state-of-the-art methods.