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CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition
Pengtao JIA Qi ZHAO Boze LI Jing ZHANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2021/08/01
Online ISSN: 1745-1361
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.