Gait recognition based on Gabor wavelets and modified gait energy image for human identification

Deng-Yuan Huang; Ta-Wei Lin; Wu-Chih Hu; Chih-Hsiang Cheng

doi:10.1117/1.JEI.22.4.043039

20 December 2013 Gait recognition based on Gabor wavelets and modified gait energy image for human identification

Deng-Yuan Huang, Ta-Wei Lin, Wu-Chih Hu, Chih-Hsiang Cheng

Author Affiliations +

Funded by: National Science Council, Taiwan

Journal of Electronic Imaging, Vol. 22, Issue 4, 043039 (December 2013). https://doi.org/10.1117/1.JEI.22.4.043039

Abstract

This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.

Citation Download Citation

Deng-Yuan Huang, Ta-Wei Lin, Wu-Chih Hu, and Chih-Hsiang Cheng "Gait recognition based on Gabor wavelets and modified gait energy image for human identification," Journal of Electronic Imaging 22(4), 043039 (20 December 2013). https://doi.org/10.1117/1.JEI.22.4.043039

Published: 20 December 2013

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