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.
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.
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|Item Type:||Conference Paper|
|Keywords:||Gait, Backfilled gait energy image, Compressive sensing, Depth camera, Kinect, Frontal view gait recognition, Side view gait recognition, 3D reconstruction|
|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|
|Copyright Owner:||Copyright 2012 please consult the authors|
|Deposited On:||14 Jan 2013 02:09|
|Last Modified:||19 Jun 2015 04:32|
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