Parallel Heat Kernel Volume Based Local Binary Pattern on Multi-Orientation Planes for Face Representation

Appropriate representation is one of the keys to successful face recognition technologies. Actual facial appearance sometimes differs dramatically because of variations in pose, illumination, expression, and occlusion. However, existing face representation methods remain insufficiently powerful and...

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Published in:	International journal of parallel programming Vol. 46; no. 5; pp. 943 - 962
Main Authors:	Lu, Wei, Yang, Xiaomin, Gou, Xu, Jian, Lihua, Wu, Wei, Jeon, Gwanggil
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.10.2018 Springer Nature B.V
Subjects:	Algorithms Computer programming Computer Science Face Face recognition Feature extraction Kernels Macrostructure Occlusion Orientation Parallel processing Pattern analysis Pattern recognition Planes Processor Architectures Representations Software Engineering/Programming and Operating Systems Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm Theory of Computation Face presentation Face recognition Parallel multi-scale heat kernel face Local binary patterns
ISSN:	0885-7458, 1573-7640
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Abstract	Appropriate representation is one of the keys to successful face recognition technologies. Actual facial appearance sometimes differs dramatically because of variations in pose, illumination, expression, and occlusion. However, existing face representation methods remain insufficiently powerful and robust. Hence, we propose a new feature extraction approach for face representation based on heat kernel volume and local binary patterns. Multi-scale heat kernel faces are created in our proposed framework. We then reformulate these multi-scale heat kernel faces as three-dimensional volume. Furthermore, we generate multi-orientation planes from the heat kernel volume, which reflects orientation co-occurrence statistics among different heat kernel faces. Finally, we apply local binary pattern (LBP) analysis on the multi-orientation planes of the heat kernel volume to capture the microstructure and macrostructure of face appearance. Hence, we obtain the heat kernel volume based local binary pattern on multi-orientation planes (HKV–LBP–MOP) descriptor. The proposed method is successfully be paralleled. We applied the method to face recognition and obtain the performance of 99.28 and 87.82% on ORL and Yale databases respectively. Experimental results on the show that the proposed algorithm significantly outperforms other well-known approaches in terms of recognition rate.
AbstractList	Appropriate representation is one of the keys to successful face recognition technologies. Actual facial appearance sometimes differs dramatically because of variations in pose, illumination, expression, and occlusion. However, existing face representation methods remain insufficiently powerful and robust. Hence, we propose a new feature extraction approach for face representation based on heat kernel volume and local binary patterns. Multi-scale heat kernel faces are created in our proposed framework. We then reformulate these multi-scale heat kernel faces as three-dimensional volume. Furthermore, we generate multi-orientation planes from the heat kernel volume, which reflects orientation co-occurrence statistics among different heat kernel faces. Finally, we apply local binary pattern (LBP) analysis on the multi-orientation planes of the heat kernel volume to capture the microstructure and macrostructure of face appearance. Hence, we obtain the heat kernel volume based local binary pattern on multi-orientation planes (HKV–LBP–MOP) descriptor. The proposed method is successfully be paralleled. We applied the method to face recognition and obtain the performance of 99.28 and 87.82% on ORL and Yale databases respectively. Experimental results on the show that the proposed algorithm significantly outperforms other well-known approaches in terms of recognition rate.
Author	Jian, Lihua Wu, Wei Lu, Wei Jeon, Gwanggil Yang, Xiaomin Gou, Xu
Author_xml	– sequence: 1 givenname: Wei surname: Lu fullname: Lu, Wei organization: School of Software Engineering, Beijing Jiaotong University, College of Electronics and Information Engineering, Sichuan University – sequence: 2 givenname: Xiaomin surname: Yang fullname: Yang, Xiaomin email: arielyang@scu.edu.cn organization: College of Electronics and Information Engineering, Sichuan University – sequence: 3 givenname: Xu surname: Gou fullname: Gou, Xu organization: College of Electronics and Information Engineering, Sichuan University – sequence: 4 givenname: Lihua surname: Jian fullname: Jian, Lihua organization: College of Electronics and Information Engineering, Sichuan University – sequence: 5 givenname: Wei surname: Wu fullname: Wu, Wei organization: College of Electronics and Information Engineering, Sichuan University – sequence: 6 givenname: Gwanggil surname: Jeon fullname: Jeon, Gwanggil organization: College of Information and Technology, Incheon National University
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Cites_doi	10.1109/AFGR.2008.4813354 10.1109/TIP.2002.999679 10.1016/S0031-3203(99)00139-9 10.1016/j.eswa.2017.02.034 10.1109/IPIN.2013.6851448 10.1016/j.procs.2013.06.001 10.1007/978-3-540-27868-9_20 10.1155/2014/523862 10.1109/12.210173 10.1109/34.598227 10.1016/j.media.2013.09.007 10.1109/TMI.2016.2550080 10.1109/TBME.2015.2503756 10.1109/TNNLS.2012.2234134 10.1109/34.598235 10.1016/j.neucom.2017.01.064 10.1162/089976603321780317 10.1109/SITIS.2014.16 10.1109/TPAMI.2005.55 10.1109/TIFS.2016.2601065 10.1109/34.598228 10.1016/0031-3203(95)00067-4 10.1109/TPAMI.2010.128 10.1109/LSP.2009.2036653 10.1007/s11045-015-0363-2 10.1109/TPAMI.2007.1110 10.1109/TNNLS.2016.2527796
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Intell.200729691592810.1109/TPAMI.2007.1110 – reference: MoghaddamBPentlandAProbabilistic visual learning for object representationIEEE Trans. Pattern Anal. Mach. Intell.199719769671010.1109/34.598227 – reference: ZhanZCaiJ-FGuoDLiuYChenZXiaoboQFast multiclass dictionaries learning with geometrical directions in MRI reconstructionIEEE Trans. Biomed. Eng.20166391850186110.1109/TBME.2015.2503756 – reference: ChenYHaoCWenWEnhuaWRobust dense reconstruction by range merging based on confidence estimationSci. China Inf. Sci.2016599111 – reference: BaiXiaoHancockEdwin R.Heat Kernels, Manifolds and Graph EmbeddingLecture Notes in Computer Science2004Berlin, HeidelbergSpringer Berlin Heidelberg198206 – reference: Palma, G., Comerci, M., Alfano, B., Cuomo, S., De Michele, P., Piccialli, F., Borrelli, P.: 3D Non-local means denoising via multi-GPU. 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SubjectTerms	Algorithms Computer programming Computer Science Face Face recognition Feature extraction Kernels Macrostructure Occlusion Orientation Parallel processing Pattern analysis Pattern recognition Planes Processor Architectures Representations Software Engineering/Programming and Operating Systems Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm Theory of Computation
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Title	Parallel Heat Kernel Volume Based Local Binary Pattern on Multi-Orientation Planes for Face Representation
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