Abstract
The illumination problem is one of the main bottlenecks in a practical face recognition system. Illumination preprocessing is an effective way to handle lighting variations for robust face recognition. In this paper, we present a novel illumination insensitive image, namely directional gradients integration image (DGII), for illumination insensitive face recognition. Unlike the existing model-based methods, the DGII is generated directly from the decomposed gradient components of a logarithmic image, without involving any training procedure. Based on the Lambertian reflectance model, we first calculate the horizontal and vertical gradients in the logarithmic domain to eliminate the illumination component. Secondly, to utilize the gradient orientation information, the two gradients are further decomposed into four components along four directions. Then, the four directional gradients are integrated to reconstruct an illumination insensitive image using anisotropic diffusion. Finally, the reconstructed image is fused with the gradient magnitude image through weighted summing. For performance evaluation, we simply use principal component analysis for feature extraction, Euclidean distance as similarity measure and nearest-neighbor classifier for face recognition. Experiments on the Yale B, the extended Yale B and the CMU PIE (The Carnegie Mellon University pose, illumination and expression database) face databases show that the proposed method provides better results than some state-of-the-art methods, showing its effectiveness for illumination normalization.
Similar content being viewed by others
Notes
References
Adini, Y., Moses, Y., Ullman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 721–732 (1997)
Basri, R., Jacobs, D.W.: Lambertian reflectance and linear subspaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 218–233 (2003)
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)
Belhumeur, P.N., Kriegman, D.J.: What is the set of images of an object under all possible illumination conditions? Int. J. Comput. Vis. 28(3), 245–260 (1998)
Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(9), 1519–1524 (2006)
Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001)
Georghiades, A.S., Kriegman, D.J., Belhurneur, P.: Illumination cones for recognition under variable lighting: faces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998, pp. 52–58. IEEE (1998)
Gross, R., Brajovic, V.: An image preprocessing algorithm for illumination invariant face recognition. In: Kittler J, Nixon MS (eds) Audio-and Video-Based Biometric Person Authentication, pp. 10–18. Springer (2003)
Horn, B.: Robot Vision. MIT press, Cambridge, MA (1986)
Jobson, D.J., Rahman, Zu, Woodell, G., et al.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)
Kawamura, A., Yura, K., Hayama, T., Hidai, Y., Minamikawa, T., Tanaka, A., Masuda, S.: Online recognition of freely handwritten Japanese characters using directional feature densities. In: Proceedings of 11th IAPR International Conference on Pattern Recognition, 1992, Vol. II. Conference B: Pattern Recognition Methodology and Systems, pp. 183–186. IEEE (1992)
Lee, J., Moghaddam, B., Pfister, H., Machiraju, R.: A bilinear illumination model for robust face recognition. In: 10th IEEE International Conference on Computer Vision, 2005, ICCV 2005, vol. 2, pp. 1177–1184. IEEE (2005)
Lee, K.C., Ho, J., Kriegman, D.: Nine points of light: acquiring subspaces for face recognition under variable lighting. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, CVPR 2001, vol. 1, pp. I–519. IEEE (2001)
Lee, K.C., Ho, J., Kriegman, D.J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Ramamoorthi, R., Hanrahan, P.: On the relationship between radiance and irradiance: determining the illumination from images of a convex lambertian object. JOSA A 18(10), 2448–2459 (2001)
Samsung, S.: Integral normalized gradient image a novel illumination insensitive representation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops, pp. 166–166. IEEE (2005)
Savvides, M., Kumar, B.V.: Illumination normalization using logarithm transforms for face authentication. In: Kittler J, Nixon MS (eds) Audio-and Video-Based Biometric Person Authentication, pp. 549–556. Springer (2003)
Shan, S., Gao, W., Cao, B., Zhao, D.: Illumination normalization for robust face recognition against varying lighting conditions. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003, AMFG 2003, pp. 157–164. IEEE (2003)
Shashua, A., Riklin-Raviv, T.: The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 129–139 (2001)
Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression (PIE) database. In: Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition, 2002, pp. 46–51. IEEE (2002)
Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)
Wang, H., Li, S.Z., Wang, Y.: Face recognition under varying lighting conditions using self quotient image. In: Proceedings of 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004, pp. 819–824. IEEE (2004)
Wiskott, L., Fellous, J.M., Kuiger, N., Von Der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)
Xie, X., Zheng, W.S., Lai, J., Yuen, P.C., Suen, C.Y.: Normalization of face illumination based on large-and small-scale features. IEEE Trans. Image Process. 20(7), 1807–1821 (2011)
Zhang, L., Samaras, D.: Face recognition under variable lighting using harmonic image exemplars. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, vol. 1, pp. I–19. IEEE (2003)
Zhao, W., Chellappa, R.: Robust Face Recognition Using Symmetric Shape-from-Shading. Computer Vision Laboratory, Center for Automation Research, University of Maryland, College Park (1999)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)
Zou, X., Kittler, J., Messer, K.: Illumination invariant face recognition: a survey. In: 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007, pp. 1–8. IEEE (2007)
Acknowledgements
This work is supported by National Natural Science Foundation of China under Grant #61472125.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhao, X., Lin, Y., Ou, B. et al. Directional gradients integration image for illumination insensitive face representation. Machine Vision and Applications 29, 815–825 (2018). https://doi.org/10.1007/s00138-018-0935-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-018-0935-x