Research Article
An Optimized Algorithm on Multi-view Transform for Gait Recognition
@INPROCEEDINGS{10.1007/978-3-030-06161-6_16, author={Lingyun Chi and Cheng Dai and Jingren Yan and Xingang Liu}, title={An Optimized Algorithm on Multi-view Transform for Gait Recognition}, proceedings={Communications and Networking. 13th EAI International Conference, ChinaCom 2018, Chengdu, China, October 23-25, 2018, Proceedings}, proceedings_a={CHINACOM}, year={2019}, month={1}, keywords={Gait recognition Gait energy image View transform model Principal component analysis}, doi={10.1007/978-3-030-06161-6_16} }- Lingyun Chi
Cheng Dai
Jingren Yan
Xingang Liu
Year: 2019
An Optimized Algorithm on Multi-view Transform for Gait Recognition
CHINACOM
Springer
DOI: 10.1007/978-3-030-06161-6_16
Abstract
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 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 .
