2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-5.3
Paper Title EFFECTIVE GAIT FEATURE EXTRACTION USING TEMPORAL FUSION AND SPATIAL PARTIAL
Authors Yifan Chen, School of Computer Science and School of Artificial Intelligence, Optics and Electronics (iOPEN), China; Yang Zhao, Xuelong Li, School of Artificial Intelligence, Optics and Electronics (iOPEN), China
SessionMLR-APPL-IVASR-5: Machine learning for image and video analysis, synthesis, and retrieval 5
LocationArea C
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Gait recognition provides a more convenient way for human identification, as it can identify person with less cooperation and intrusion compared to other biometric features. Current gait recognition frameworks utilize a template to extract temporal feature or regard the whole person as a unit, and they obtain limited temporal information and fine-grained features. To overcome this problem, we propose a network consisting of two parts: Temporal Feature Fusion (TFF) and Fine-grained Feature Extraction (FFE). First, we extract the most representative temporal information from raw gait sequences by TFF. Next, we use the idea of partial features on fused temporal features to extract more fine-grained spatial block features. It is worth mentioning that the proposed algorithm provides an effective feature extraction framework for complex gait recognition, as it focuses on the temporal fusion for representative information, and the extraction of the fine grained spatial features. Extensive experiments illustrated that we have an outstanding performance on CASIA-B and mini-OUMVLP compared to other state-of-the-art methods including GaitSet and GaitNet. In particularly, the average rank-1 accuracy of all probe views on normal walking condition (NM) achieve 95.7%.