A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample
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- 作者：Yinjie Leia ; yinjie@scu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Yulan Guob ; yulan.guo@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Munawar Hayatc ; d ; hayatm@au1.ibm.com" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Mohammed Bennamounc ; mohammed.bennamoun@uwa.edu.au" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Xinzhi Zhoua ; xz.zhou@scu.edu.cn" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：3D face recognition ; 3D representation ; Sparse representation ; Partial facial data ; Single sample problem
- 刊名：Pattern Recognition
- 出版年：2016
- 出版时间：April 2016
- 年：2016
- 卷：52
- 期：Complete
- 页码：218-237
- 全文大小：3263 K
文摘
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Novel Keypoint-based Multiple Triangle Statistics (KMTS) are proposed for 3D face representation.
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The proposed local descriptor is robust to partial facial data and expression/pose variations.
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A Two-Phase Weighted Collaborative Representation Classification (TPWCRC) framework is used to perform face recognition.
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The proposed classification framework can effectively address the single sample problem.
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State-of-the-art performance on six challenging datasets with high efficiency is achieved.