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    An efficient gait recognition method for known and unknown covariate conditions

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    Publication date
    2021-01
    Author
    Bukhari, M.
    Bajwa, K.B.
    Gillani, S.
    Maqsood, M.
    Durrani, M.Y.
    Mehmood, Irfan
    Ugail, Hassan
    Rho, S.
    Keyword
    Gait recognition
    Covariate conditions
    Discriminative feature learning
    FLDA
    Rights
    This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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    Abstract
    Gait is a unique non-invasive biometric form that can be utilized to effectively recognize persons, even when they prove to be uncooperative. Computer-aided gait recognition systems usually use image sequences without considering covariates like clothing and possessions of carrier bags whilst on the move. Similarly, in gait recognition, there may exist unknown covariate conditions that may affect the training and testing conditions for a given individual. Consequently, common techniques for gait recognition and measurement require a degree of intervention leading to the introduction of unknown covariate conditions, and hence this significantly limits the practical use of the present gait recognition and analysis systems. To overcome these key issues, we propose a method of gait analysis accounting for both known and unknown covariate conditions. For this purpose, we propose two methods, i.e., a Convolutional Neural Network (CNN) based gait recognition and a discriminative features-based classification method for unknown covariate conditions. The first method can handle known covariate conditions efficiently while the second method focuses on identifying and selecting unique covariate invariant features from the gallery and probe sequences. The feature set utilized here includes Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Haralick texture features. Furthermore, we utilize the Fisher Linear Discriminant Analysis for dimensionality reduction and selecting the most discriminant features. Three classifiers, namely Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron are used for gait recognition under strict unknown covariate conditions. We evaluated our results using CASIA and OUR-ISIR datasets for both clothing and speed variations. As a result, we report that on average we obtain an accuracy of 90.32% for the CASIA dataset with unknown covariates and similarly performed excellently on the ISIR dataset. Therefore, our proposed method outperforms existing methods for gait recognition under known and unknown covariate conditions.
    URI
    http://hdl.handle.net/10454/18805
    Version
    Published version
    Citation
    Bukhari M, Bajwa KB, Gillani S et al (2021) An efficient gait recognition method for known and unknown covariate conditions. IEEE Access. 9: 6465-6477.
    Link to publisher’s version
    https://doi.org/10.1109/ACCESS.2020.3047266
    Type
    Article
    Collections
    Engineering and Informatics Publications

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