Histogram of weighted local directions for gait recognition
Sivapalan, Sabesan, Chen, Daniel, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2013) Histogram of weighted local directions for gait recognition. In Proceedings of 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Oregon Convention Center, Portland, OR, pp. 125-130.
In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.
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|Item Type:||Conference Paper|
|Keywords:||Gait energy image, HOG, MDA, PCA, LDP, GEV, SRC|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Copyright Owner:||Copyright 2013 IEEE|
|Copyright Statement:||Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.|
|Deposited On:||25 Sep 2013 23:11|
|Last Modified:||09 Oct 2013 23:54|
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