An adaptive Illumination Preprocessing method for Face Recognition

Illumination variation is one of the most important challenges in face recognition, because changes to illumination conditions have a significant effect on performance. However, most illumination preprocessing methods apply the same level of processing to all facial images, without considering their unique illumination conditions. Therefore, the performances of existing preprocessing methods are limited when dealing with varying illumination conditions. In this paper, we propose an adaptive illumination preprocessing method for face recognition, which adaptively preprocesses each face image according to its illumination condition. The proposed method first uses the illumination quality index (IQI) to describe the illumination. Then, we create an adaptive parameter adjustment model for the illumination preprocessing method. Finally, the proposed model adjusts the parameters of the illumination preprocessing method based on the IQI of the facial image, and enhances the preprocessing effect. Our extensive simulation results show that the proposed method can effectively improve the performance of face recognition under varying illumination conditions, when compared with existing methods.

[1]  Xinnan Fan,et al.  A detection and classification approach for underwater dam cracks , 2016 .

[2]  Jiuzhen Liang,et al.  Different lighting processing and feature extraction methods for efficient face recognition , 2014, IET Image Process..

[3]  Ehsanollah Kabir,et al.  Visual illumination compensation for face images using light mapping matrix , 2013, IET Image Process..

[4]  Jian-Huang Lai,et al.  Normalization of Face Illumination Based on Large-and Small-Scale Features , 2011, IEEE Transactions on Image Processing.

[5]  Wen Gao,et al.  Lighting Aware Preprocessing for Face Recognition across Varying Illumination , 2010, ECCV.

[6]  Rabab Kreidieh Ward,et al.  Adaptive Region-Based Image Enhancement Method for Robust Face Recognition Under Variable Illumination Conditions , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Xiaofei Zhou,et al.  Kernel subclass convex hull sample selection method for SVM on face recognition , 2010, Neurocomputing.

[8]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 Large-Scale Experimental Results , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Sabah Jassim,et al.  Image-Quality-Based Adaptive Face Recognition , 2010, IEEE Transactions on Instrumentation and Measurement.

[10]  Roberto Cipolla,et al.  A methodology for rapid illumination-invariant face recognition using image processing filters , 2009, Comput. Vis. Image Underst..

[11]  Dorin Comaniciu,et al.  Total variation models for variable lighting face recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Kin-Man Lam,et al.  An efficient illumination normalization method for face recognition , 2006, Pattern Recognit. Lett..

[13]  Meng Joo Er,et al.  Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Haitao Wang,et al.  Generalized quotient image , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[15]  Wen Gao,et al.  Illumination normalization for robust face recognition against varying lighting conditions , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[16]  Ralph Gross,et al.  An Image Preprocessing Algorithm for Illumination Invariant Face Recognition , 2003, AVBPA.

[17]  Tieniu Tan,et al.  Fusion of global and local features for face verification , 2002, Object recognition supported by user interaction for service robots.

[18]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[19]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[20]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Masaki Onishi,et al.  Generation of obstacle Avoidance Based on Image Features and embodiment , 2012, Int. J. Robotics Autom..

[22]  Dong Ren,et al.  A Robust Processing Chain for Face Recognition under Varying Illumination , 2011, Intell. Autom. Soft Comput..

[23]  Jingying Chen,et al.  Multi-Cue Facial Feature Detection and Tracking under Various Illuminations , 2010, Int. J. Robotics Autom..

[24]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.