It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for f...It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry restoration/neighborhood replacement. Our experiment results show that the proposed method performs better than the existing LBP-based methods at the point of illumination preprocessing. Moreover, compared with some face image preprocessing methods, such as histogram equalization, Gamma transformation, Retinex, and simplified LBP operator, our method can effectively improve the robustness for face recognition against illumination variation, and achieve higher recog- nition rate.展开更多
Differences in illumination of the same face can defeat simple face recognition systems,yet most methods that compensate are too difficult to implement. Local quotient image (LQI) is an efficient illumination preproce...Differences in illumination of the same face can defeat simple face recognition systems,yet most methods that compensate are too difficult to implement. Local quotient image (LQI) is an efficient illumination preprocessing method for face recognition systems. An illumination model and a face model were developed,and their use in the new method was analyzed. Analysis of the method's computational complexity showed it to be efficient. Experimental results on Yale Face Database B showed that the method can effectively eliminate the effects of differences in illumination and provides considerable improvement in recognition rates.展开更多
文摘It is one of the major challenges for face recognition to minimize the disadvantage of il- lumination variations of face images in different scenarios. Local Binary Pattern (LBP) has been proved to be successful for face recognition. However, it is still very rare to take LBP as an illumination preprocessing approach. In this paper, we propose a new LBP-based multi-scale illumination pre- processing method. This method mainly includes three aspects: threshold adjustment, multi-scale addition and symmetry restoration/neighborhood replacement. Our experiment results show that the proposed method performs better than the existing LBP-based methods at the point of illumination preprocessing. Moreover, compared with some face image preprocessing methods, such as histogram equalization, Gamma transformation, Retinex, and simplified LBP operator, our method can effectively improve the robustness for face recognition against illumination variation, and achieve higher recog- nition rate.
文摘Differences in illumination of the same face can defeat simple face recognition systems,yet most methods that compensate are too difficult to implement. Local quotient image (LQI) is an efficient illumination preprocessing method for face recognition systems. An illumination model and a face model were developed,and their use in the new method was analyzed. Analysis of the method's computational complexity showed it to be efficient. Experimental results on Yale Face Database B showed that the method can effectively eliminate the effects of differences in illumination and provides considerable improvement in recognition rates.
