The backfilled GEI : a cross-capture modality gait feature for frontal and side-view gait recognition

Sivapalan, Sabesan, Chen, Daniel, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2012) The backfilled GEI : a cross-capture modality gait feature for frontal and side-view gait recognition. In Proceedings of the 2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Institute of Electrical and Electronic Engineers (IEEE) , Fremantle, W. A, pp. 1-8.

View at publisher

Abstract

In this paper, we propose a novel direction for gait recognition research by proposing a new capture-modality independent, appearance-based feature which we call the Back-filled Gait Energy Image (BGEI). It can can be constructed from both frontal depth images, as well as the more commonly used side-view silhouettes, allowing the feature to be applied across these two differing capturing systems using the same enrolled database. To evaluate this new feature, a frontally captured depth-based gait dataset was created containing 37 unique subjects, a subset of which also contained sequences captured from the side. The results demonstrate that the BGEI can effectively be used to identify subjects through their gait across these two differing input devices, achieving rank-1 match rate of 100%, in our experiments. We also compare the BGEI against the GEI and GEV in their respective domains, using the CASIA dataset and our depth dataset, showing that it compares favourably against them. The experiments conducted were performed using a sparse representation based classifier with a locally discriminating input feature space, which show significant improvement in performance over other classifiers used in gait recognition literature, achieving state of the art results with the GEI on the CASIA dataset.

Impact and interest:

3 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

236 since deposited on 14 Jan 2013
38 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 56359
Item Type: Conference Paper
Refereed: Yes
Keywords: Gait, Backfilled gait energy image, Compressive sensing, Depth camera, Kinect, Frontal view gait recognition, Side view gait recognition, 3D reconstruction
DOI: 10.1109/DICTA.2012.6411694
ISBN: 978-1-4673-2180-8
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Funding:
Copyright Owner: Copyright 2012 please consult the authors
Deposited On: 14 Jan 2013 02:09
Last Modified: 19 Jun 2015 04:32

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page