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Prosperous Human Gait Recognition: an end-to-end system based on pre-trained CNN features selection

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Abstract

Human Gait Recognition (HGR) is a biometric approach, widely used for security purposes from the past few decades. In HGR, the change in an individual walk along with wearing clothes and carrying bag are major covariant controls which impact the performance of a system. Moreover, recognition under various view angles is another key challenge in HGR. In this work, a novel fully automated method is proposed for HGR under various view angles using deep learning. Four primary steps are involved such as: preprocessing of original video frames, exploiting pre-trained Densenet-201 CNN model for features extraction, reduction of additional features from extracted vector based on a hybrid selection method, and finally recognition using supervised learning methods. The extraction of CNN features is a key step in which our target is to extract the most active features. To achieve this goal, we fuse the features of both second last and third last layers in a parallel process. At a later stage, best features are selected by the Firefly algorithm and Skewness based approach. These selected features are serially combined and fed to One against All Multi Support Vector Machine (OAMSVM) for final recognition. Three different angles 180, 360 and 540 of the CASIA B dataset are selected for the evaluation process and accuracy of 94.3%, 93.8% and 94.7% is achieved respectively. Results show significant improvement in accuracy and recall rate as compared to the existing state-of-the-art techniques.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, Pakistan

    Asif Mehmood & Muhammad Sharif

  2. Department of Computer Science and Engineering, HITEC University, Museum Road Taxila, Rawalpindi, Pakistan

    Muhammad Attique Khan & Imran Ashraf

  3. Department of Software Engineering, Foundation University Islamabad, Islamabad, Pakistan

    Sajid Ali Khan & Muhammad Shaheen

  4. Department of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

    Tanzila Saba

  5. SEECS, National University of Science and Technology, Islamabad, Pakistan

    Naveed Riaz

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  1. Asif Mehmood
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Correspondence to Muhammad Attique Khan.

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Mehmood, A., Khan, M.A., Sharif, M. et al. Prosperous Human Gait Recognition: an end-to-end system based on pre-trained CNN features selection. Multimed Tools Appl 83, 14979–14999 (2024). https://doi.org/10.1007/s11042-020-08928-0

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