Gait recognition using histograms of temporal gradients

Jashila Nair Mogan, Chin Poo Lee, Kian Ming Lim

Research output: Journal PublicationConference articlepeer-review

5 Citations (Scopus)


In this paper, we present a gait recognition method using convolutional features and histograms of temporal gradients. The method comprises three stages, namely the convolutional stage, temporal gradient stage and classification stage. In the convolutional stage, the video frames are convolved with a set of pre-learned filters. This is followed by the temporal gradient stage. In this stage, the gradient of each convolved frame in time axis is calculated. Unlike histograms of oriented gradients that accumulate the gradients in the spatial domain, the proposed histogram of temporal gradients encodes the gradients in the spatial and temporal domain. The histogram of temporal gradients captures the gradient patterns of every pixel over the temporal axis throughout the video sequence. By doing so, the feature encodes both spatial and temporal information in the gait cycle. Finally, in the classification stage, a majority voting classification with Euclidean distance is performed for gait recognition. Experimental results show that the proposed method outperforms the state-of-the-art methods.

Original languageEnglish
Article number012051
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 17 Jun 2020
Externally publishedYes
EventInternational Conference on Telecommunication, Electronic and Computer Engineering 2019, ICTEC 2019 - Melaka, Malaysia
Duration: 22 Oct 201924 Oct 2019

ASJC Scopus subject areas

  • General Physics and Astronomy


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