CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs

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Published: Sep 21, 2023
DOI: https://doi.org/10.17762/ijritcc.v12i1.7905
Keywords:
Gait, Multidomain, Frequency, Entropy, Connected, Body, Components, GAN, Scenarios

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Ashish Kumar Misal
Computer Science & Engineering Department, Rungta College of Engineering and Technology Bhilai, National Institute of Technology, Raipur, India
Abha Choubey
Computer Science & Engineering Department, Shri shankaracharya technical campus, Bhilai, India
Siddharth Choubey
Shri shankaracharya technical campus, Bhilai, India
Anil Kumar Pandey
Department Of Information Technology, National Institute Of Technology Raipur, Raipur, India

Abstract

This Gait identification assists in recognition of human body components from temporal image sequences. Such components consist of connected-body entities including head, upper body, lower body regions. Existing Gait recognition models use deep learning methods including variants of Convolutional Neural Networks (CNNs), Q-Learning, etc. But these methods are either highly complex, or do not perform well under complex background conditions. Moreover, most of these models are validated on a specific environmental condition, and cannot be scaled for general-purpose deployments. To overcome these issues, this text proposes design of a novel cross-domain bioinspired model for identification of gait components via Iterated Generative Adversarial Networks (IGANs). The proposed model initially extracts multidomain pixel-level feature sets from different images. These include frequency components via Fourier analysis, entropy components via Cosine analysis, spatial components via Gabor analysis, and window-based components via Wavelet &Convolutional analysis. These feature sets are processed via a Grey Wolf Optimization (GWO) Model, which assists in identification of high-density & highly variant features for different gait components. These features are classified via an iterated GAN, which comprises of Generator & Discriminator ssModels that assist in evaluating connected body components. These operations generate component-level scores that assist in identification of gait from complex background images. Due to which, the proposed model was observed to achieve 9.5% higher accuracy, 3.4% higher precision, and 2.9% higher recall than existing gait identification methods. The model also uses iterative learning, due to which its accuracy is incrementally improved w.r.t. number of evaluated image sets.

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How to Cite
Misal, A. K. ., Choubey, A. ., Choubey, S. ., & Pandey, A. K. . (2023). CDBMGCIG: Design of a Cross-Domain Bioinspired Model for identification of Gait Components via Iterated GANs. International Journal on Recent and Innovation Trends in Computing and Communication, 12(1), 10–18. https://doi.org/10.17762/ijritcc.v12i1.7905
Issue
Vol. 12 No. 1 (2024)
Section
Articles

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