Stacked Progressive Auto-Encoders for Clothing-Invariant Gait Recognition

Gait recognition has been considered as an unique and useful biometric for person identification at distance. However, variations in covariate factors such as view angles, clothing, and carrying condition can alter an individual's gait pattern. These variations make the task of gait analysis much more complicated. Recognizing different subjects under clothing variations remains one of the most challenging tasks in gait recognition. In this paper, we propose a Stacked Progressive Auto-encoders (SPAE) model for clothing-invariant gait recognition. A key contribution of this work is to directly learn clothing-invariant gait features for gait recognition in a progressive way by stacked multi-layer auto-encoders. In each progressive auto-encoder, our SPAE is designed to transform the Gait Energy Images (GEI) with complicated clothing types to ones of normal clothing, while keeping the GEI with normal clothing type unchanged. As a result, it gradually reduces the effect of appearance changes due to variations of clothes. The proposed method is evaluated on the challenging clothing-invariant gait recognition OU-ISIR Treadmill dataset B. The experimental results demonstrate that the proposed method can achieve a far better performance compared to existing works.