World Academy of Science, Engineering and Technology
Published September 25, 2012 | Version 6318
Journal article Open

2D Spherical Spaces for Face Relighting under Harsh Illumination

Description

In this paper, we propose a robust face relighting technique by using spherical space properties. The proposed method is done for reducing the illumination effects on face recognition. Given a single 2D face image, we relight the face object by extracting the nine spherical harmonic bases and the face spherical illumination coefficients. First, an internal training illumination database is generated by computing face albedo and face normal from 2D images under different lighting conditions. Based on the generated database, we analyze the target face pixels and compare them with the training bootstrap by using pre-generated tiles. In this work, practical real time processing speed and small image size were considered when designing the framework. In contrast to other works, our technique requires no 3D face models for the training process and takes a single 2D image as an input. Experimental results on publicly available databases show that the proposed technique works well under severe lighting conditions with significant improvements on the face recognition rates.

Files

6318.pdf

Files (412.6 kB)

Name Size
md5:84313ce57e16777cae06f6965660cbec
412.6 kB Preview Download

Additional details

References

  • P. S, "On the individuality of fingerprints," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1010-1025, 2002.
  • L. Ma, T. Tan, and D. Zhang, "Personal identification based on iris texture analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519-1533, 2003.
  • P. Hallinan, "A low-dimensional representation of human faces for arbitrary lighting conditions," In Procceedings of IEEE Conference on Computer Vision and Pattern Recogition, pp. 995-999, 1994.
  • A. Samil and P. Iyengar, "Automatic recognition and analysis of human faces and facial expressions: A survey," Journal of Foo, pp. 65-75, 1992.
  • L. Wiskott, J. Fellous, N. Kruger, and C. Malsburg, "Face recognition by elastic bunch graph matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, 1997.
  • M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-96, 1991.
  • H. Murase and S. Nayar, "Visual recognition of 3-d objects from appearance," International Journal of Computer Vision, vol. 14, no. 1, pp. 5-24, 1995.
  • Y. Adini, Y. Moses, and S. Ullman, "Face recognition: The problem of compensating for hanges in illumination directions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 3, pp. 721- 732, 1997.
  • A. Shashua and T. Riklin, "The quotient image: Class-based re-rendering and recognition with varying illuminations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 129-139, 2001. [10] P. Belhumeur, "What is the set of images of an object under all possible lighting conditions?" IEEE conference on Computer Vision and Pattern Recognition, 1996. [11] A. Georghiades and D. Kriegman, "From few to many: illumination cone models for face recognition under variable lighting and pose," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643-660, 2001. [12] R. Basri and D. Jacobs, "Lambertian reflectance and linear subspaces," IEEE International Conference on Computer Vision, pp. 383-390, 2001. [13] K. Lee, J. Ho, and D. Kriegman, "Nine points of light: Acquiring subspaces for face recognition under variable lighting," IEEE Conference on Vision and Pattern Recognition, pp. 519-525, 2001. [14] L. Zhang and D. Samaras, "Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 351-363, 2006. [15] D. Samaras and D. Metaxas, "Coupled lighting direction and shape estimation from single images," IEEE International Conference on Computer Vision, vol. 2, pp. 868-874, 1999. [16] C. Liu and H. Wechsler, "Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition," IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467-476, 2002. [17] J. Yang, D. Zhang, and A. Frangi, "Two-dimensional pca: a new approach to appearance-based face representation and recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, 2004. [18] K. Kim, K. Jung, and H. J. Kim, "Face recognition using kernel principal component analysis," IEEE Signal Processing Letters, vol. 9, no. 2, pp. 40-42, 2002. [19] J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, 2009.