NDLI: Illumination invariant face recognition using SQI and weighted LBP histogram

Content Provider	IEEE Xplore Digital Library
Author	Biglari, M. Mirzaei, F. Ebrahimpour-Komeh, H.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Eng., Univ. of Kashan, Kashan, Iran (Biglari, M.; Mirzaei, F.; Ebrahimpour-Komeh, H.)
Abstract	The illumination variation is one of the main challenges in real-world face recognition systems. Face recognition under uneven illumination is still an open problem. In this paper, we proposed a novel illumination invariant face recognition approach based on Self Quotient Image and weighted Local Binary Pattern. We improved the performance of the system by using different sigma values of SQI for training and testing. Furthermore, using two multi-region uniforms LBP for feature extraction simultaneously, made the system more robust to illumination variation. This approach gathers information of the image in both local and global levels. The weighted Chi square statistic is used for histogram comparison. The used weighted approach emphasizes on the more important regions in faces. The proposed approach is compared with some methods like QI, SQI, QIR, MQI, DMQI, DSFQI, PCA and LDA on Yale face database B and CMU-PIE database. The experimental results show that our method outperforms other tested methods.
Starting Page	1
Ending Page	7
File Size	995882
Page Count	7
File Format	PDF
ISBN	9781467362047
e-ISBN	9781467362061
DOI	10.1109/PRIA.2013.6528433
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-06
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Local Binary Pattern Histograms Databases Face recognition Lighting Feature extraction Quotient Image Face Illumination Normalization Face Recognition
Content Type	Text
Resource Type	Article

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