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Big Data Mining and Analytics  2021, Vol. 4 Issue (4): 223-232    DOI: 10.26599/BDMA.2021.9020006
    
Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception
Changjie Wang(),Zhihua Li*(),Benjamin Sarpong()
Department of Computer Science and Technology, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
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Abstract  

Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice.



Key wordsgait recognition      person identification      deep learning      multimodal feature fusion     
Received: 22 October 2020      Published: 30 August 2021
Fund:  Smart Manufacturing New Model Application Project Ministry of Industry and Information Technology(ZH-XZ-18004);Future Research Projects Funds for Science and Technology Department of Jiangsu Province(BY2013015-23);Fundamental Research Funds for the Ministry of Education(JUSRP211A41);Fundamental Research Funds for the Central Universities(JUSRP42003);the 111 Project(B2018)
Corresponding Authors: Zhihua Li     E-mail: thewang@gmail.com;zhli@jiangnan.edu.cn;6181910030@stu.jiangnan.edu.cn
About author: Changjie Wang received the BEng degree from Changsha University of Science & Technology, China in 2018. He is currently a master student at Jiangnan University, China. His research interests include the Internet of Things (IoTs) and cyber security.|Zhihua Li received the BEng degree from Wuxi Light Industry University, China in 1992, and the MEng and PhD degrees from Jiangnan University, China in 2002 and 2009, respectively. He is currently an associate professor at Jiangnan University. His research interests include network technology, parallel/distributed computing, information security, edge computing, and mobile computing.|Benjamin Sarpong received the BEng degree from Kwame Nkrumah University of Science and Technology, Ghana in 2014, and the MEng degree from Jiangnan University, China in 2020. He is currently an experimentalist at Jiangnan University. His research interests include sensor network, mobile computing, and their information security.
Cite this article:

Changjie Wang,Zhihua Li,Benjamin Sarpong. Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception. Big Data Mining and Analytics, 2021, 4(4): 223-232.

URL:

http://bigdata.tsinghuajournals.com/10.26599/BDMA.2021.9020006     OR     http://bigdata.tsinghuajournals.com/Y2021/V4/I4/223

Fig. 1 Line chart of individual gait information demo.
Fig. 2 ReLU function graph.
Fig. 3 Max pooling demo.
Fig. 4 CNN-GR infrastructure.
Fig. 5 Unimodal gait feature recognition model.
Fig. 6 Gait feature fusion-based identity-recognition scheme.
Fig. 7 Recognition rate varies with the number of trainings.
Fig. 8 Comparison of average recognition rates.
Fig. 9 Confusion matrix comparison of different algorithms.
DatasetAlgorithmKappa indicator
MIT-GaitBPNN0.792
SGFI0.811
LBNet0.823
MGII0.834
Infocom06-GaitBPNN0.781
SGFI0.805
LBNet0.813
MGII0.828
Table 1 Kappa indicator comparison.
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