Gait analysis and recognition using angular transforms

An angular representation is proposed for gait analysis and recognition applications. Each human silhouette in a gait sequence is transformed into a low dimensional feature vector consisting of average pixel distances from the center of the silhouette. The proposed approach is very suitable for the processing of imperfectly segmented silhouettes since it is robust to segmentation errors. The sequence of feature vectors corresponding to a gait sequence is used for identification based on a minimum-distance criterion between test and reference sequences. By using the new transform on the gait challenge database, concrete improvements in recognition performance are seen in comparison to other methods of similar or higher complexity.