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Gait recognition based on modified phase-only correlation

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Abstract

Gait recognition is an emerging biometric technology aiming to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because it is noncontact and does not require the subject’s cooperation. Clothing, carrying conditions and other intra-class variations, also referred to as “covariates,” affect the performance of gait recognition systems. This paper proposes a supervised feature extraction method, which is able to select relevant discriminative features for human recognition to mitigate the impact of covariates and hence improve the recognition performances. The proposed method is evaluated using the CASIA gait database (dataset B), and the experimental results suggest that our method yields 81.40 % of correct classification when compared against similar techniques which do not exceed 77.96 %.

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Notes

  1. In the case of MPOC is multiplied by the spectral weighting function.

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Authors and Affiliations

  1. LITIS EA 4108 - INSA de Rouen, Saint-Étienne du Rouvray, France

    Imad Rida

  2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Somaya Almaadeed

  3. Department of Computer Science and Digital Technologies, Northumbria University, Newcastle upon Tyne, UK

    Ahmed Bouridane

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  1. Imad Rida
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  2. Somaya Almaadeed
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  3. Ahmed Bouridane
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Correspondence to Imad Rida.

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Rida, I., Almaadeed, S. & Bouridane, A. Gait recognition based on modified phase-only correlation. SIViP 10, 463–470 (2016). https://doi.org/10.1007/s11760-015-0766-4

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  • DOI: https://doi.org/10.1007/s11760-015-0766-4

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