Gait recognition based on modified phase-only correlation

Imad Rida, Somaya Al-Maadeed, Ahmed Bouridane

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)


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 %.
Original languageEnglish
Pages (from-to)463-470
JournalSignal, Image and Video Processing
Issue number3
Early online date21 Mar 2015
Publication statusPublished - 1 Mar 2016


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