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Deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image

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Abstract

Gait recognition is a well-known biometric identification technology and is widely employed in different fields. Due to the advantages of deep learning, such as self-learning capability, high accuracy and excellent generalization ability, various deep network algorithms have been applied in biometric recognition. Numerous studies have been conducted in this area; however, they may not always yield the expected outcomes owing to the issue of data imbalance in clinical and healthcare industries. To overcome this problem, deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image (HGR-DMCSCN) is proposed in this manuscript. Initially, the input images are taken from CASIA B and OU-ISIR datasets. Then the input images are given to preprocessing segment to enhance the superiority of the images based upon contrast-limited adaptive histogram equalization filtering (CLAHEF). Then preprocessed image is given to classification process using deep multi-convolutional stacked capsule network (DMCSCN) that is utilized for human gait detection under various conditions, like normal walking, carrying a bag and wearing a cloth. The proposed HGR-DMCSCN approach is executed in python and its performance is examined under performance metrics, such as F-Score, accuracy, RoC and computational time. Finally, the proposed approach attains 28.70%, 11.87% and 14.79% higher accuracy for CASIA B compared with existing methods.

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Authors and Affiliations

  1. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India

    P. Nithyakani

  2. Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, 603203, Tamil Nadu, India

    M. Ferni Ukrit

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  1. P. Nithyakani
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  2. M. Ferni Ukrit
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PN (corresponding author) was responsible for conceptualization, methodology and original draft preparation. MFU was involved in supervision.

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Correspondence to P. Nithyakani.

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Nithyakani, P., Ferni Ukrit, M. Deep multi-convolutional stacked capsule network fostered human gait recognition from enhanced gait energy image. SIViP 18, 1375–1382 (2024). https://doi.org/10.1007/s11760-023-02851-1

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  • DOI: https://doi.org/10.1007/s11760-023-02851-1

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