Recursive spatiotemporal subspace learning for gait recognition
对步态空时数据的连续特征子空间分析

A gait recognition method based on spatiotemporal feature extraction is proposed. Recursive subspace learning is used to extract both time and space feature of gait. In the first subspace learning, the periodic dynamic feature of gait is extracted by principal component analysis and sequence data is represented in the periodicity feature vector form. In the second subspace learning, principal component analysis plus linear discriminant analysis are applied to the oeriodicity feature vector representation of gait and sequence data is compressed into gait feature vector. gait feature vector is an effective representation because it contains both human dynamic and shape feature. Experimental result on the USF gait database shows that the proposed method achieves highly competitive performance with respect to other published gait recognition approaches.