Probabilistic Model-Based Silhouette Refinement for Gait Recognition

An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed, and a relatively complete and high quality silhouette is obtained. The silhouettes are sequentially refined in two levels according to two different probabilistic models. The first level is within-sequence refinement. Each silhouette in a particular sequence is refined by an individual model trained by the gait images from current sequence. The second level is between-sequence refinement. All the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of pedestrians. The intention is to preserve the within-class similarity and to decrease the interaction between one class and others. Comparative experimental results indicate that the proposed algorithm is simple and quite effective, and it helps the existing recognition methods achieve a higher recognition performance.