A cascade fusion scheme for gait and cumulative foot pressure image recognition

Cumulative foot pressure images represent the 2D ground reaction force during one gait cycle. Biomedical and forensic studies show that humans can be distinguished by unique limb movement patterns and ground reaction force. Considering continuous gait pose images and corresponding cumulative foot pressure images, this paper presents a cascade fusion scheme to represent the potential connections between them and proposes a two-modality fusion based recognition system. The proposed scheme contains two stages: (1) given cumulative foot pressure images, canonical correlation analysis is employed to retrieve corresponding gait pose image candidates in gallery dataset; (2) pedestrian recognition is achieved via small samples matching between retrieved gait pose images and unlabeled ones. The proposed fusion recognition system is not only insensitive to slight changes of environment and the individual users, but also can be extended to multiple biometrics retrieval. Experimental results are conducted on the CASIA gait–footprint dataset, which contains cumulative foot pressure images and its corresponding gait pose image sequence from 88 subjects. Evaluation results suggest the effectiveness of the proposed scheme compared to other related approaches.