Histogram of weighted local directions for gait recognition
Data(s) |
23/06/2013
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Resumo |
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. |
Formato |
application/pdf |
Identificador | |
Relação |
http://eprints.qut.edu.au/62889/3/62889a.pdf DOI:10.1109/CVPRW.2013.26 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. |
Direitos |
Copyright 2013 IEEE 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. |
Fonte |
Science & Engineering Faculty |
Palavras-Chave | #090609 Signal Processing #Gait energy image #HOG #MDA #PCA #LDP #GEV #SRC |
Tipo |
Conference Paper |