A Re-Lighting Algorithm to Improve Recall Rate of Face Recognition

doi:10.3969/j.issn.1006-2475.2015.06.011

Abstract

Abstract: In escapee hunting, face recognition is applied widely. However, face recognition rate is affected by complex lighting environment. So it is a key problem that how to diminish the effect of illumination for face recognition. A re-lighting algorithm is proposed in this paper. First, the inherent texture features of the input image is extracted by selfquotient image algorithm. Then the illumination of the image is normalized based on an illumination bootstrap set. Finally the re-lighting image is produced by the combining texture image and the normalized illumination image. Experiments on AR, CMU_PIE and CAS_Lighting Face Database demonstrate that the proposed algorithm can improve the recall rate of face recognition algorithm, and further help to chase escapee.

Key words: re-lighting algorithm, face recognition, Lambertain model

CLC Number:

TP391

LUO Guang-ming. A Re-Lighting Algorithm to Improve Recall Rate of Face Recognition[J]. Computer and Modernization, 2015, 0(6): 51-54.

References

［1］ Adini Y, Moses Y, Ullman S. Face recognition: The problem of compensating for changes in illumination direction［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):721-732.
［2］ Lades M, Vorbruggen J C, Buhmann J, et al. Distortion invariant object recognition in the dynamic link architecture［J］. IEEE Transactions on Computers, 1993,42(3):300-311.
［3］ Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(7):971-987.
［4］ Pizer S M, Amburn E P, Austin J D, et al. Adaptive histogram equalization and its variations［J］. Computer Vision, Graphics, and Image Processing, 1987,39（3）：355-368.
［5］ Shan Shiguang, Gao Wen, Cao Bo, et al. Illumination normalization for robust face recognition against varying lighting conditions［C］// IEEE International Workshop on Analysis and Modeling of Faces and Gestures 2003:157-164.
［6］ Jobson D J, Rahman Z, Woodell G A. Properties and performance of a center/surround retinex［J］. IEEE Transactions on Image Processing, 1997,6(3):451-462.
［7］ Amnon Shashua, Tammy RiklinRaviv. The quotient image: Class-based re-rendering and recognition with varying illuminations［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014，23（2）：129-139
［8］ Riklin-Raviv T, Shashua A. The quotient image: Class based recognition and synthesis under varying illumination［C］// IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999,2:566-571.
［9］ Wang Haitao, Li S Z, Wang Yangsheng. Face recognition under varying lighting conditions using self quotient image［C］// The 6th IEEE International Conference on Automatic Face and Gesture Recognition. 2004:819-824.
［10］Tan Xiaoyang, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting conditions［J］. IEEE Transactions on Image Processing, 2010,19(6):1635-1650.
［11］Chen T, Yin W, Zhou X S, et al. Total variation models for variable lighting face recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(9):1519-1524.
［12］Liu Jianyi, Zheng Nanning, Xiong Lei, et al. Illumination transition image:Parameter-based illumination estimation and rerendering［C］// The 19th International Conference on Pattern Recognition. 2008:1-4.
［13］Xie Xiaohua, Zheng Wei-shi, Lai Jianhuang, et al. Normalization of face illumination based on large-and small-scale features［J］. IEEE Transaction on Image Processing, 2011,20(7):1807-1821.
［14］Turk M, Pentland A. Eigenfaces for recognition［J］. Journal of Cognitive Neuroscience, 1991,3(1):71-86.
［15］Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. fisherfaces:Recognition using class specific linear projection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7):711-720.
［16］Ke Yan, Chen Youbin, Zhang David. Gabor surface feature for face recognition［C］// IEEE 2011 1st Asian Conference on Pattern Recognition (ACPR). 2011:288-292.
［17］Sim T, Baker S, Bsat M. The CMU pose, illumination, and expression database［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(12):1615-1618.
［18］Gao Wen, Cao Bo, Shan Shiguang, et al. The CASPEAL largescale Chinese face database and baseline evaluations［J］. IEEE Transactions on Systems,Man and CyberneticsPart A：Systems and Humans, 2008,38(1):149-161.

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