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Local Zernike moment and multiscale patch-based LPQ for face recognition. (English) Zbl 07058543
Jia, Yingmin (ed.) et al., Proceedings of 2016 Chinese intelligent systems conference, Xiamen, China. Volume II. Singapore: Springer (ISBN 978-981-10-2334-7/hbk; 978-981-10-2335-4/ebook). Lecture Notes in Electrical Engineering 405, 19-27 (2016).
Summary: In this paper, a novel feature extraction method combining Zernike moment with multiscale patch-based local phase quantization is introduced, which can deal with the problem of uncontrolled image conditions in face recognition, such as expressions, blur, occlusion, and illumination changes (EBOI). First, the Zernike moments are computed around each pixel other than the whole image and then double moment images are, respectively, constructed from the real and imaginary parts. Subsequently, multiscale patch-based local phase quantization descriptor is utilized for the non-overlapping patches of moment images to obtain the texture information. Afterward, the support vector machine (SVM) is employed for classification. Experimental results performed on ORL, JAFFE, and AR databases clearly show that the LZM-MPLPQ method outperforms the state-of-the-art methods and achieves better robustness against severe conditions abovementioned.
For the entire collection see [Zbl 1385.00002].
MSC:
68 Computer science
94 Information and communication theory, circuits
Software:
JAFFE; LIBSVM
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