An Optimized Algorithm on Multi-view Transform for Gait Recognition
Gait is one of the common used biometric features for human recognition, however, for some view angles, it is difficult to exact distinctive features, which leads to hindrance for gait recognition. Considering the challenge, this paper proposes an optimized multi-view gait recognition algorithm, which creates a Multi-view Transform Model (VTM) by adopting Singular Value Decomposition (SVD) on Gait Energy Image (GEI). To achieve the goal above, we first get the Gait Energy Image (GEI) from the gait silhouette data. After that, SVD is used to build the VTM, which can convert the gait view-angles to \( 90^\circ \) to get more distinctive features. Then, considering the image matrix is so large after SVD in practice, Principal Component Analysis (PCA) is used in our experiments, which helps to reduce redundancy. Finally, we measure the Euclidean distance between gallery GEI and transformed GEI for recognition. The experimental result shows that our proposal can significantly increase the richness of multi-view gait features, especially for angles offset to \( 90^\circ \).
KeywordsGait recognition Gait energy image View transform model Principal component analysis
This work was supported by the Fundamental Research Funds for the Central Universities on the grant ZYGX2015Z009, and also supported by Applied Basic Research Key Programs of Science and Technology Department of Sichuan Province under the grant 2018JY0023.
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