A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 2
Apr.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Songmin Jia, Lijia Wang and Xiuzhi Li, "View-invariant Gait Authentication Based on Silhouette Contours Analysis and View Estimation," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 2, pp. 226-232, 2015.
Citation: Songmin Jia, Lijia Wang and Xiuzhi Li, "View-invariant Gait Authentication Based on Silhouette Contours Analysis and View Estimation," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 2, pp. 226-232, 2015.

View-invariant Gait Authentication Based on Silhouette Contours Analysis and View Estimation


This work was supported by National Natural Science Foundation of China (61105033, 61175087).

  • In this paper, we propose a novel view-invariant gait authentication method based on silhouette contours analysis and view estimation. The approach extracts Lucas-Kanade based gait flow image and head and shoulder mean shape (LKGFI-HSMS) of a human by using the Lucas-Kanade's method and procrustes shape analysis (PSA). LKGFI-HSMS can preserve the dynamic and static features of a gait sequence. The view between a person and a camera is identified for selecting the target's gait feature to overcome view variations. The similarity scores of LKGFI and HSMS are calculated. The product rule combines the two similarity scores to further improve the discrimination power of extracted features. Experimental results demonstrate that the proposed approach is robust to view variations and has a high authentication rate.


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