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基于几何模型的单幅人脸图像光照补偿方法

蔺蘭 赵戈 唐延东 田建东 何思远

蔺蘭, 赵戈, 唐延东, 田建东, 何思远. 基于几何模型的单幅人脸图像光照补偿方法. 自动化学报, 2013, 39(12): 2090-2099. doi: 10.3724/SP.J.1004.2013.02090
引用本文: 蔺蘭, 赵戈, 唐延东, 田建东, 何思远. 基于几何模型的单幅人脸图像光照补偿方法. 自动化学报, 2013, 39(12): 2090-2099. doi: 10.3724/SP.J.1004.2013.02090
LIN Lan, ZHAO Ge, TANG Yan-Dong, TIAN Jian-Dong, HE Si-Yuan. Illumination Compensation for Face Recognition Using Only One Image. ACTA AUTOMATICA SINICA, 2013, 39(12): 2090-2099. doi: 10.3724/SP.J.1004.2013.02090
Citation: LIN Lan, ZHAO Ge, TANG Yan-Dong, TIAN Jian-Dong, HE Si-Yuan. Illumination Compensation for Face Recognition Using Only One Image. ACTA AUTOMATICA SINICA, 2013, 39(12): 2090-2099. doi: 10.3724/SP.J.1004.2013.02090

基于几何模型的单幅人脸图像光照补偿方法

doi: 10.3724/SP.J.1004.2013.02090

Illumination Compensation for Face Recognition Using Only One Image

Funds: 

Supported by National Natural Science Foundation of China (61102116)

More Information
    Corresponding author: TANG Yan-Dong
  • 摘要: 为减少光照对人脸识别的影响,本文提出了一种以补偿角度和(Sum of Compensated Angle)为不变量的光照补偿新方法. 首先,补偿角度和是临界补偿状态下两幅图像的光照角度之和. 对某单光源系统,该不变量仅由光照系统决定且为定值. 其次,根据人类头骨在法兰克福截面的形状特性,我们提出了包含人头骨结构的几何人脸光照模型. 据此模型,补偿角度由不变量和待补偿图像的光照角度计算得出,从而将光照补偿转化为简单加法操作. 最后,在Yale B人脸数据库上的补偿结果表明了算法的有效性. 较Sang-Ⅱ Choi的方法显著地提高了大角度下的补偿效果,且在水平和竖直方向上更加鲁棒.
  • [1] Minakshi G, RupKumar D, Deepjyoti M, Rupam D, Manasjyoti B. A secured template based face recognition technique. Computer Science and Information Technology, 2012, 2(3): 153-166
    [2] Okere I, Van Niekerk J, Carroll M. Assessing information security culture: a critical analysis of current approaches. In: Proceedings of the 2012 Information Security for South Africa. Johannesburg, Gauteng: IEEE, 2012. 1-8
    [3] Farahmand F, Atallah M J, Spafford E H. Incentive alignment and risk perception: an information security application. IEEE Transactions on Engineering Management, 2013, 60(2): 238-246
    [4] Schouten B, Jacobs B. Biometrics and their use in e-passports. Image and Vision Computing, 2009, 27(3): 305-312
    [5] Shu Y C, Gu Y, Chen J M. Dynamic authentication with sensory information for the access control systems. IEEE Transactions on Parallel and Distributed Systems, 2013, doi: 10.1109/TPDS.2013.153
    [6] Wu D L, Ng W W Y, Chan P P K, Ding H L, Jing B Z, Daniel S. Access control by RFID and face recognition based on neural network. In: Proceedings of the 9th International Conference on Machine Learning and Cybernetics. Qingdao, China: ICMLC, 2010. 675-680
    [7] Fang Wei-Tao, Ma Peng, Cheng Zheng-Bin, Yang Dan, Zhang Xiao-Hong. Two-dimensional projective non-negative matrix factorization and its application to face recognition. Acta Automatica Sinica, 2012, 38(9): 1503-1512 (in Chinese)
    [8] Zheng Jian-Wei, Wang Wan-Liang, Yao Xiao-Ming, Shi Hai-Yan. Face recognition using tensor local Fisher discriminant analysis. Acta Automatica Sinica, 2012, 38(9): 1485-1495 (in Chinese)
    [9] Zhao Hai-Ying, Yang Yi-Fan, Xu Zheng-Guang. 3D facial gender classification based on multi-angle LBP feature. Acta Automatica Sinica, 2012, 38(9): 1544-1549 (in Chinese)
    [10] Basavaraj A, Nagaraj P. The facial features extraction for face recognition based on geometrical approach. In: Proceedings of the 2006 Conference on Electrical and Computer Engineering. Ottawa, Canada: CCECE, 2006. 1936-1939
    [11] Cheng Yong-Qing, Liu Ke, Yang Jing-Yu, Wang Hua-Feng. A robust algebraic method for human face recognition. In: Proceedings of the 11th International Conference on Pattern Recognition. The Hague: IEEE, 1992. 221-224
    [12] Fortuna J, Capson D. ICA filters for lighting invariant face recognition. In: Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE, 2004. 334-337
    [13] Nara Y, Yang J M, Suematsu Y. Face recognition using improved principal component analysis. In: Proceedings of the 2003 International Symposium on Micromechatronics and Human Science. Nagoya, Japan: IEEE, 2003. 77-82
    [14] Nefian A V, Hayes M H. Hidden Markov models for face recognition. In: Proceedings of the 1998 International Conference on Acoustics, Speech and Signal Processing. Seattle, USA: IEEE, 1998. 2721-2724
    [15] Yong R, Iftekharuddin K M, White W E. Recurrent network-based face recognition using image sequences. In: Proceedings of the 2009 Symposium on Computational Intelligence for Multimedia Signal and Vision Processing. Nashville, USA: IEEE, 2009. 41-46
    [16] Wright J, Gang H. Implicit elastic matching with random projections for pose-variant face recognition. In: Proceedings of the 2009 Conference on Computer Vision and Pattern Recognition. Miami, USA: IEEE, 2009. 1502-1509
    [17] Amor B, Ardabilian M, Chen L M. New experiments on ICP-based 3D face recognition and authentication. In: Proceedings of the 18th international conference on Pattern Recognition. Hong Kong, China: IEEE, 2006. 1195-1199
    [18] Russ T, Koch M, Little C. A 2D range Hausdorff approach for 3D face recognition. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 1063-6919
    [19] Lei Y Q, Li Q M, Song X B, Shi Z X, Chen D J. 3D face hierarchical recognition based on geometric and curvature features. In: Proceedings of the 2009 International Symposium on Computer Network and Multimedia Technology. Wuhan, China: IEEE, 2009. 1-4
    [20] Bowyer K, Chang K, Flynn P. A survey of approaches to three-dimensional face recognition. In: Proceedings of the 17th International Conference on Pattern Recognition. Cambridge, UK: IEEE, 2004. 358-361
    [21] Lao S H, Sumi Y, Kawade M, Tomita F. 3D template matching for pose invariant face recognition using 3D facial model built with isoluminance line based stereo vision. In: Proceedings of the 15th International conference on Pattern Recognition. Barcelona, Spain: IEEE, 2000. 911-916
    [22] Lee J, Milios E. Matching range images of human faces. In: Proceedings of the 1990 International Conference on Computer Vision. Osaka, Japan: IEEE, 1990. 722-726
    [23] Wang Y M, Pan G, Wu Z H. 3D face recognition in the presence of expression: a guidance-based constraint deformation approach. In: Proceedings of the 2007 Conference on Computer Vision and Pattern Recognition. Minneapolis, USA: IEEE, 2007. 1-7
    [24] Li S Z, Chu R F, Liao S C, Zhang L. Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(4): 627-639
    [25] Wu S Q, Wei L Z, Fang Z J. Radiation-energy based infrared face recognition. Journal of Image and Graphics, 2007, 12(10): 1845-1848
    [26] Miao Z W, Ji W, Xu Y, Yang J. A novel ultrasonic sensing based human face recognition. In: Proceedings of the 2008 International Ultrasonics Symposium. Beijing, China: IEEE, 2008. 1873-1876
    [27] Nishiyama M, Yamaguchi O. Face recognition using the classified appearance-based quotient image. In: Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition. Southampton, UK: IEEE, 2006. 5-54
    [28] Georghiades A, Belhumeur P, Kriegman D. From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 643-660
    [29] Georghiades A S, Kriegman D, Belhurneur P N. Illumination cones for recognition under variable lighting: faces. In: Proceedings of the 1998 Conference on Computer Vision and Pattern Recognition. Santa Barbara, USA: IEEE, 1998. 52-58
    [30] He X F, Yan S C, Hu Y X, Niyogi P, Zhang H J. Face recognition using Laplacian faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340
    [31] Chen C P, Chen C S. Intrinsic illumination subspace for lighting insensitive face recognition. IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42(2): 422-433
    [32] Tzimiropoulos G, Zafeiriou S, Pantic M. Subspace learning from image gradient orientations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 34(12): 2454-2466
    [33] Sekkati H, Laganiére R, Mitiche A, Youmaran R. Robust background subtraction using geodesic active contours in ICA subspace for video surveillance applications. In: Proceedings of the 9th Conference on Computer and Robot Vision. Toronto, Canada: IEEE, 2012. 190-197
    [34] Zhang D, He J Z. Feature space based face super-resolution reconstruction. Acta Automatica Sinica, 2012, 38(7): 1145-1152 (in Chinese)
    [35] Zhang L, Samaras D. Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(3): 351-363
    [36] Liu P J, Wang Y H, Huang D, Zhang Z X, Chen L M. Learning the spherical harmonic features for 3-D face recognition. IEEE Transactions on Image Processing, 2013, 22(3): 914-925
    [37] Althloothi S, Mahoor M, Voyles R. A robust method for rotation estimation using spherical harmonics representation. IEEE Transactions on Image Processing, 2013, 22(6): 2306-2316
    [38] Liem M, Gavrila D. Person appearance modeling and orientation estimation using spherical harmonics. In: Proceedings of the 2013 International Conference and Workshops on Automatic Face and Gesture Recognition. Shanghai, China: IEEE, 2013. 1-6
    [39] Zhao W Y, Chellappa R. Illumination-insensitive face recognition using symmetric shape-from-shading. In: Proceedings of the 2000 Conference on Computer Vision and Pattern Recognition, Hilton Head Island, USA: IEEE, 2000. 286-293
    [40] Choi S I, Kima C, Choia C H. Shadow compensation in 2D images for face recognition. Pattern Recognition, 2007, 40(7): 2118-2125
    [41] Farkas L G. Anthropometry of the Head and Face. New York: Lippincott, Williams and Wilkins, 1994
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出版历程
  • 收稿日期:  2011-11-17
  • 修回日期:  2013-06-26
  • 刊出日期:  2013-12-20

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