Hidden Conditional Random Fields for Gait Recognition

Gait is a recent important research field among the computer vision community. It aims identifying humans by analyzing their walk. It has different advantage comparing to others biometrics technologies such as face recognition, iris recognition and fingerprint. It can be performed at distance and without subject cooperation. Also, it doesn't need high resolution of image. In this paper, we present a new discriminative method for gait recognition using hyprid conditional random fields (CRF). We use a Hidden CRF model to combine two classifiers; a spatial classifier which assigns a label to a local feature (SURF descriptors) and temporal classifier which uses a motion History Image (MHI). The proposed framework, firstly extracts the human silhouette. Secondly, it takes out spatial and temporal cues from each frame. Then, it applies the MLP classification to the two set of features to obtain the Hidden CRF input; the final step is recognizing person with HCRF. Experimental results showed the superiority of our proposed method over several state of arts.