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https://hdl.handle.net/2440/60883
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Type: | Journal article |
Title: | Heat kernel based local binary pattern for face representation |
Author: | Li, X. Hu, W. Zhang, Z. Wang, H. |
Citation: | IEEE Signal Processing Letters, 2010; 17(3):308-311 |
Publisher: | IEEE-Inst Electrical Electronics Engineers Inc |
Issue Date: | 2010 |
ISSN: | 1070-9908 1558-2361 |
Statement of Responsibility: | Xi Li, Weiming Hu, Zhongfei Zhang and Hanzi Wang |
Abstract: | Face classification has recently become a very hot research topic in computer vision and multimedia information processing. It has many potential applications, in which face representation is the most fundamental task. Most existing face representation methods perform poorly in capturing the intrinsic structural information of face appearance. To address this problem, we propose a novel multiscale heat kernel based face representation, for heat kernels perform well in characterizing the topological structural information of face appearance. Further, the local binary pattern (LBP) descriptor is incorporated into the multiscale heat kernel face representation for the purpose of capturing texture information of face appearance. As a result, we have the heat kernel based local binary pattern (HKLBP) descriptor. Finally, a Support Vector Machine (SVM) classifier is learned in the HKLBP feature space for face classification. Experimental results demonstrate the effectiveness and superiority of our face classification framework. |
Rights: | © Copyright 2010 IEEE – All Rights Reserved |
DOI: | 10.1109/LSP.2009.2036653 |
Published version: | http://dx.doi.org/10.1109/lsp.2009.2036653 |
Appears in Collections: | Aurora harvest Computer Science publications |
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