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Local pattern-based illumination compensation in face images
Author(s): Min Yao
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Paper Abstract

Facial illumination severely affects the face recognition performance; thus, it should be finely compensated beforehand. This paper mainly studies three representative illumination-insensitive representation methods including GRF, WF and LBP, all of which consider using the local pattern information to extract facial features. We first present the ideas and theories of each method. Then based on the reflectance model, the underlying connections of GRF and WF are discussed through showing the deduction of GRF and WF how they exclude the luminance component and are only related to the intrinsic facial features. We also give the explanation about the correspondence of LBP to the reflectance model. Finally, experiments on a standard but challenging illuminated face database are conducted, in which GRF, WF and LBP are tested and compared to other illumination normalization methods in terms of face recognition rate.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106934 (6 May 2019); doi: 10.1117/12.2524201
Show Author Affiliations
Min Yao, Shanghai Maritime Univ. (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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