Gait recognition for person re-identification

Omar Elharrouss*, Noor Almaadeed, Somaya Al-Maadeed, Ahmed Bouridane

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)
10 Downloads (Pure)


Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.

Original languageEnglish
Pages (from-to)3653-3672
Number of pages20
JournalJournal of Supercomputing
Issue number4
Early online date27 Aug 2020
Publication statusPublished - 1 Apr 2021


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