CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition

Pengtao JIA  Qi ZHAO  Boze LI  Jing ZHANG  

IEICE TRANSACTIONS on Information and Systems   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)
image classification,  gait recognition,  deep learning,  convolutional neural networks,  attention mechanism,  

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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.