DocumentCode :
730264
Title :
Quantized fuzzy LBP for face recognition
Author :
Jianfeng Ren ; Xudong Jiang ; Junsong Yuan
Author_Institution :
Inst. of Media Innovation, Nanyang Technol. Univ., Singapore, Singapore
fYear :
2015
fDate :
19-24 April 2015
Firstpage :
1503
Lastpage :
1507
Abstract :
Face recognition under large illumination variations is challenging. Local binary pattern (LBP) is robust to illumination variation, but sensitive to noise. Fuzzy LBP (FLBP) partially solves the noise-sensitivity problem by incorporating fuzzy logic in the representation of local binary patterns. The fuzzy membership function is determined by both sign and magnitude of the pixel difference. However, the magnitude is easily altered by noise, hence could be unreliable. Thus, we propose to determine the fuzzy membership function by its sign only. We name the proposed approach as Quantized Fuzzy LBP (QFLBP). On two challenging face recognition datasets, it is shown more robust to noise, and demonstrates a superior performance to FLBP and many other LBP variants.
Keywords :
face recognition; fuzzy logic; fuzzy set theory; pattern classification; LBP; face recognition; fuzzy membership function; illumination variations; incorporating fuzzy logic; local binary pattern; local binary patterns; noise sensitivity problem; pixel difference; quantized fuzzy LBP; Face; Noise; Reliability; Face Recognition; Fuzzy Local Binary Pattern; Quantized Fuzzy LBP;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
Type :
conf
DOI :
10.1109/ICASSP.2015.7178221
Filename :
7178221
Link To Document :
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