Gait style and gait content: bilinear models for gait recognition using gait re-sampling

Human identification using gait is a challenging computer vision task due to the dynamic motion of gait and the existence of various sources of variations such as viewpoint, walking surface, clothing, etc. In this paper we propose a gait recognition algorithm based on bilinear decomposition of gait data into time-invariant gait-style and time-dependent gait-content factors. We developed a generative model by embedding gait sequences into a unit circle and learning nonlinear mapping, which facilitates synthesis of temporally, aligned gait sequences. Given such synthesized gait data, bilinear model is used to separate invariant gait style, which is used for recognition. We also show that the recognition can be generalized to new situations by adapting the gait-content factor to the new condition and therefore obtain corrected gait-styles for recognition.