Speed-Invariant Gait Recognition

Abstract

We propose a method of speed-invariant gait recognition in a unified framework of model-and appearance-based approaches. When a person changes his/her walking speed, kinematic features (e.g., stride and joint angle) are changed while static features (e.g., thigh and shin lengths) are preserved. Based on the fact, firstly, static and kinematic features are decoupled from a gait silhouette sequence by fitting a human model. Secondly, a factorization-based stride transformation model for the kinematic features is created by using a training set for multiple non recognition-target persons on multiple speeds. This model can transform the kinematic features from a gallery speed to another arbitrary probe speed. Because only the kinematic features are insufficient to achieve a high recognition performance, we therefore finally synthesize a gait silhouette sequence by combining the preserved static features and the transformed kinematic features for matching. Experiments with the OU-ISIR Gait Database show the effectiveness of the proposed method.