关键词: 人脸识别, 低秩分解, 相对梯度, 光照健壮性

Abstract: To further enhance the illumination robustness of facial gradient features,an illumination robust low-rank relative gradient histogram feature extraction method (LR-RGHF) is proposed based on the property that low-rank decomposition can separate the intrinsic characteristic and sparse noise of an image effectively.In the first place,the relative gradient magnitude image and gradient direction information of each pixel is obtained by doing relative gradient operation on face image.In the next place,in order to remove the edge error caused by uneven illumination distribution,the relative gradient magnitude image is decomposed into its low-rank component and sparse noise component using low-rank decomposition.Finally,the low-rank component of relative gradient magnitude image is decomposed into several sub images according to its gradient direction information,each of the sub images is then filtered and encoded using local binary pattern to form the LR-RGHF.Experimental on the classical FERET subset and representative illumination subsets,YaleB and PIE subsets,illustrated that the recognition performance of LR-RGHF outperforms the recognition performances of relative gradient histogram feature (RGHF),patterns of oriented edge magnitudes (POEM) and low rank facial feature.On YaleB subset,the recognition accuracies of LR-RGHF are at least 4% higher than RGHF.Experimental results demonstrate that LR-RGHF reveals strong illumination robustness for face recognition,especially for uneven illumination distribution.

Key words: face recognition, low-rank decomposition, relative gradient, illumination robustness