关键词: 人脸识别, 单样本, 局部二值模式, 加权

Abstract: Facing with the problem of face recognition with single training sample per person, the conventional methods will suffer serious performance drop or even fail to work. To solve this problem, this paper proposes an adaptive weighted Local Binary Pattern（LBP） method. It combines both texture feature and topological information with a novel weighted way involving the variance of sub-images. The paper partitions facial images and uses LBP to extract texture feature. It makes use of variance to implement the adaptive weighted fusion for features. The nearest neighbor classifier is adopted for further recognition. Experimental results show a better performance on ORL facial database.

Key words: face recognition, single sample, Local Binary Pattern（LBP）, weightiness