IEEE Access | |
Gait Recognition and Re-Identification Based on Regional LSTM for 2-Second Walks | |
Nirattaya Khamsemanan1  Piya Limcharoen1  Cholwich Nattee1  | |
[1] Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang, Pathum Thani, Thailand; | |
关键词: Gait biometric; gait recognition; gait verification; gait-embedded vector; human recognition; information retrieval; | |
DOI : 10.1109/ACCESS.2021.3102936 | |
来源: DOAJ |
【 摘 要 】
Law enforcement and different authorities need a new efficient way to track and re-identify a person of interest via different cameras. Usually, the person of interest is not known and the original video may be short and have poor quality. In this paper, we propose a new technique based on a new regional-LSTM learning model that can use a 2-second walk to recognize and re-identify an unknown person. The proposed technique first targets the rhythm of movements in different regions of the body by creating a separate LSTM model for each region. Then, outputs from 22 regions are combined in a subnetwork to extract the relations and different degrees of uniqueness of all regions. The proposed regional LSTM model creates a gait-embedded vector to represent a 2-second walk. Experimenting on imbalanced and balanced datasets, the results show that the proposed regional LSTM model performs significantly better than the existing techniques on the Cumulative Matching Characteristic (CMC) curves and top-
【 授权许可】
Unknown