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3D convolution neural network-based person identification using gait cycles

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Abstract

Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries which are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans. It can be used to extract the exact walking features of individuals. Walking features depends on biological as well as the physical feature of the object; hence, it is unique to every individual. In this work, gait features are used to identify an individual. The steps involve object detection, background subtraction, silhouettes extraction, skeletonization, and training 3D Convolution Neural Network (3D-CNN) on these gait features. The model is trained and evaluated on the dataset acquired by CASIA—B Gait, which consists of 15,000 videos of 124 subjects’ walking pattern captured from 11 different angles carrying objects such as bag and coat. The proposed method focuses more on the lower body part to extract features such as the angle between knee and thighs, hip angle, angle of contact, and many other features. The experimental results are compared with amongst accuracies of silhouettes as datasets for training and skeletonized image as training data. The results show that extracting the information from skeletonized data yields improved accuracy.

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Acknowledgements

This research was supported by High Performance Computing Centre (HPCC), SRM Institute of Science and Technology for providing the computational facility for training, testing and obtaining the desired result. We thank Centre for Biometrics and Security Research (CASIA) for providing the dataset which consist of 15000+ video of 124 object walking in different angles.

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Authors and Affiliations

  1. School of Computing, SRM Institute of Science and Technology, Kattankulathur, India

    P. Supraja

  2. Senior Engineer, Augusta Hitech Soft Solutions, Coimbatore, India

    Rijo Jackson Tom

  3. School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia

    Ravi Shekhar Tiwari & V. Vijayakumar

  4. Computer Science Department, Director USC Machine Learning Centre, University of Southern California, Los Angeles, USA

    Yan Liu

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  1. P. Supraja
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  2. Rijo Jackson Tom
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  4. V. Vijayakumar
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  5. Yan Liu
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Correspondence to P. Supraja.

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Supraja, P., Tom, R.J., Tiwari, R.S. et al. 3D convolution neural network-based person identification using gait cycles. Evolving Systems 12, 1045–1056 (2021). https://doi.org/10.1007/s12530-021-09397-y

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