Face recognition under variable lighting using local qualitative representations

Yi Zhang; Ying Chu; Xingang Mou; Guilin Zhang

doi:10.1117/12.749831

15 November 2007 Face recognition under variable lighting using local qualitative representations

Yi Zhang, Ying Chu, Xingang Mou, Guilin Zhang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67881L (2007) https://doi.org/10.1117/12.749831
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

In this paper, a face recognition method using local qualitative representations is proposed to solve the problem of face recognition in varying lighting. Based on the observation that the ordinal relationship between the average brightness of image regions pair is invariant under lighting changes, Local Binary Mapping is defined as an illumination invariant for face recognition based on Local Binary Pattern descriptor, which extracts the local variance features of an image. For the 'symbol' feature vector, hamming distance is used as similarity measurement. It has been proved that the proposed method can provide the accuracy of 100 percent for subset 2, 3, 4 and 98.89 percent for subset 5 of the Yale facial database B when all images in subset 1 are used as gallery.

Citation Download Citation

Yi Zhang, Ying Chu, Xingang Mou, and Guilin Zhang "Face recognition under variable lighting using local qualitative representations", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67881L (15 November 2007); https://doi.org/10.1117/12.749831

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