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.
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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Pengtao JIA, Qi ZHAO, Boze LI, Jing ZHANG, "CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 8, pp. 1239-1249, August 2021, doi: 10.1587/transinf.2020BDP0010.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020BDP0010/_p
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@ARTICLE{e104-d_8_1239,
author={Pengtao JIA, Qi ZHAO, Boze LI, Jing ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition},
year={2021},
volume={E104-D},
number={8},
pages={1239-1249},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020BDP0010},
ISSN={1745-1361},
month={August},}
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TY - JOUR
TI - CJAM: Convolutional Neural Network Joint Attention Mechanism in Gait Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1239
EP - 1249
AU - Pengtao JIA
AU - Qi ZHAO
AU - Boze LI
AU - Jing ZHANG
PY - 2021
DO - 10.1587/transinf.2020BDP0010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2021
AB - 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.
ER -