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.