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Joint and collaborative representation with local Volterra kernels convolution feature for face recognition
Author(s): Guang Feng; Hengjian Li; Jiwen Dong; Xi Chen; Huiru Yang
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Paper Abstract

In this paper, we proposed a joint and collaborative representation with Volterra kernel convolution feature (JCRVK) for face recognition. Firstly, the candidate face images are divided into sub-blocks in the equal size. The blocks are extracted feature using the two-dimensional Voltera kernels discriminant analysis, which can better capture the discrimination information from the different faces. Next, the proposed joint and collaborative representation is employed to optimize and classify the local Volterra kernels features (JCR-VK) individually. JCR-VK is very efficiently for its implementation only depending on matrix multiplication. Finally, recognition is completed by using the majority voting principle. Extensive experiments on the Extended Yale B and AR face databases are conducted, and the results show that the proposed approach can outperform other recently presented similar dictionary algorithms on recognition accuracy.

Paper Details

Date Published: 10 April 2018
PDF: 6 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106150S (10 April 2018); doi: 10.1117/12.2303393
Show Author Affiliations
Guang Feng, Univ. of Jinan (China)
Shandong Provincial Key Lab. of Network Based Intelligent Computing (China)
Hengjian Li, Univ. of Jinan (China)
Shandong Provincial Key Lab. of Network Based Intelligent Computing (China)
Jiwen Dong, Univ. of Jinan (China)
Shandong Provincial Key Lab. of Network Based Intelligent Computing (China)
Xi Chen, Guizhou Normal Univ. (China)
Huiru Yang, Univ. of Jinan (China)
Shandong Provincial Key Lab. of Network Based Intelligent Computing (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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