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CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition

Pengtao JIA, Qi ZHAO, Boze LI, Jing ZHANG

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Summary :

Gait recognition distinguishes one individual from others according to the natural patterns of human gaits. Gait recognition is a challenging signal processing technology for biometric identification due to the ambiguity of contours and the complex feature extraction procedure. In this work, we proposed a new model - the convolutional neural network (CNN) joint attention mechanism (CJAM) - to classify the gait sequences and conduct person identification using the CASIA-A and CASIA-B gait datasets. The CNN model has the ability to extract gait features, and the attention mechanism continuously focuses on the most discriminative area to achieve person identification. We present a comprehensive transformation from gait image preprocessing to final identification. The results from 12 experiments show that the new attention model leads to a lower error rate than others. The CJAM model improved the 3D-CNN, CNN-LSTM (long short-term memory), and the simple CNN by 8.44%, 2.94% and 1.45%, respectively.

Publication
IEICE TRANSACTIONS on Information Vol.E104-D No.8 pp.1239-1249
Publication Date
2021/08/01
Publicized
2021/04/28
Online ISSN
1745-1361
DOI
10.1587/transinf.2020BDP0010
Type of Manuscript
Special Section PAPER (Special Section on Computational Intelligence and Big Data for Scientific and Technological Resources and Services)
Category

Authors

Pengtao JIA
  Xi'an University of Science and Technology
Qi ZHAO
  Xi'an University of Science and Technology
Boze LI
  Xi'an University of Science and Technology
Jing ZHANG
  Xi'an University of Science and Technology

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