Face Texture Reconstruction for Illumination Variant Face Recognition
In illumination variant face recognition, existing
methods extracting face albedo as light normalized image may lead to
loss of extensive facial details, with light template discarded. To
improve that, a novel approach for realistic facial texture
reconstruction by combining original image and albedo image is
proposed. First, light subspaces of different identities are established
from the given reference face images; then by projecting the original
and albedo image into each light subspace respectively, texture
reference images with corresponding lighting are reconstructed and
two texture subspaces are formed. According to the projections in
texture subspaces, facial texture with normal light can be synthesized.
Due to the combination of original image, facial details can be
preserved with face albedo. In addition, image partition is applied to
improve the synthesization performance. Experiments on Yale B and
CMUPIE databases demonstrate that this algorithm outperforms the
others both in image representation and in face recognition.
[1] Y. Adini, Y. Moses, S. Ullman, "Face recognition: the problem of
compensating for changes in illumination direction," IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 19, pp. 721-732, Jul.
1997.
[2] H. Barrow and J. Tenenbaum, "Recovering intrinsic scene characteristics
from images," Computer Vision System, pp. 3-26, 1978.
[3] T. Stockham, "Image processing in the context of a visual model," in
Proceedings of the IEEE, vol. 60, pp. 828-842, Jul. 1972.
[4] W. Chen, M. Joo Er, S. Wu, "Illumination compensation and
normalization for robust face recognition using discrete cosine transform
in logarithm domain," IEEE transactions on systems, man, and
cybernetics, vol. 36, pp. 458-466, Apr. 2006.
[5] H. Wang, Li,S.Z., Y. Wang, "Face recognition under varying lighting
conditions using self quotient image," In IEEE International Conference
on Automatic Face and Gesture Recognition, 2004, pp. 819-824.
[6] E. Land, "An alternative technique for the computation of the designator
in the retinex theory of color vision," In Proceedings of the National
Academy of Sciences of the United States of America, vol. 83, pp.
3078-3080, May. 1986.
[7] T. Chen, W.Yin, X.Zhou, Comaniciu, Huang,T.S., "Total variation
models for variable lighting face recognition," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 28, pp. 1519-1524, Sept.
2006.
[8] R. Gross, V. Brajovie, "An image preprocessing algorithm for
illumination invariant face recognitoin," In International Conference on
Audio and Video Based Biometric Person Authentication, 2003, pp.
10-18.
[9] X. Xie, W. Zheng, J. Lai, Y. P.C., "Face illumination normalization on
large and small scale features," In IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 2008, pp. 8-16.
[10] H. Han, S. Shan, X. Chen, W. Gao, "Illumination transfer using
homomorphic wavelet filtering and its application to light-insensitive face
recognition," In IEEE International Conference on Automatic Face and
Gesture Recognition, 2008, pp. 17-19.
[11] Tenenbaum. J, Freeman,W.T., "Learning bilinear models for twofactor
problems in vision," In IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, 1997, pp. 554-560.
[12] S. Lee, S. Moon, S. Lee, "Face recognition under arbitrary illumination
using illuminated exemplars," Pattern Recognition, vol. 40, pp.
1605-1620, May. 2007.
[13] K. Nishino, P. N.Belhumeur, S. K.Nayar, "Using eye reflections for face
recognition under varying illumination," In International Conference on
Computer Vision, 2005, vol. 1, pp. 519-526.
[14] A. S.Georghiades and P. N.Belhumeur, "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, pp.
643-660, Jun. 2001.
[15] Y. Wang, L. Zhang, Z. Liu, G. Hua, Z. Wen, Z. Zhang, Samaras,D., "Face
relighting from a single image under arbitrary unknown lighting
conditions," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 31, pp. 1968-1984, Nov. 2009.
[16] P. S.Wold, Kim Esbensen, "Principal component analysis,"
Chemometrics and Intelligent aboratory Systems, vol. 2, pp. 37-52, 1987.
[17] Wright J, Yang A.Y., Ganesh A., Sastry S.S., Yi Ma, "Robust face
recognition via sparse representation," IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 31, pp. 210-227, Feb. 2009.
[18] X. Xie and K. M.Lam, "An efficient illumination normalization method
for face recognition," Pattern Recognition Letters, vol. 27, pp. 609-617,
Apr. 2006.
[19] E. J.Ferwerda, M. StarkP. ShirleyJ. Ferwerda, "Photographic tome
reproduction for digital image," in Proceedings of SIGGRAPH, pp.
267-276, Jul. 2002.
[20] T. sim, S. Baker, M. Bsat, "The CMU pose, illumination, and experession
database," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 25, pp. 1615-1618, Dec. 2003.
[1] Y. Adini, Y. Moses, S. Ullman, "Face recognition: the problem of
compensating for changes in illumination direction," IEEE Transactions
on Pattern Analysis and Machine Intelligence, vol. 19, pp. 721-732, Jul.
1997.
[2] H. Barrow and J. Tenenbaum, "Recovering intrinsic scene characteristics
from images," Computer Vision System, pp. 3-26, 1978.
[3] T. Stockham, "Image processing in the context of a visual model," in
Proceedings of the IEEE, vol. 60, pp. 828-842, Jul. 1972.
[4] W. Chen, M. Joo Er, S. Wu, "Illumination compensation and
normalization for robust face recognition using discrete cosine transform
in logarithm domain," IEEE transactions on systems, man, and
cybernetics, vol. 36, pp. 458-466, Apr. 2006.
[5] H. Wang, Li,S.Z., Y. Wang, "Face recognition under varying lighting
conditions using self quotient image," In IEEE International Conference
on Automatic Face and Gesture Recognition, 2004, pp. 819-824.
[6] E. Land, "An alternative technique for the computation of the designator
in the retinex theory of color vision," In Proceedings of the National
Academy of Sciences of the United States of America, vol. 83, pp.
3078-3080, May. 1986.
[7] T. Chen, W.Yin, X.Zhou, Comaniciu, Huang,T.S., "Total variation
models for variable lighting face recognition," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 28, pp. 1519-1524, Sept.
2006.
[8] R. Gross, V. Brajovie, "An image preprocessing algorithm for
illumination invariant face recognitoin," In International Conference on
Audio and Video Based Biometric Person Authentication, 2003, pp.
10-18.
[9] X. Xie, W. Zheng, J. Lai, Y. P.C., "Face illumination normalization on
large and small scale features," In IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 2008, pp. 8-16.
[10] H. Han, S. Shan, X. Chen, W. Gao, "Illumination transfer using
homomorphic wavelet filtering and its application to light-insensitive face
recognition," In IEEE International Conference on Automatic Face and
Gesture Recognition, 2008, pp. 17-19.
[11] Tenenbaum. J, Freeman,W.T., "Learning bilinear models for twofactor
problems in vision," In IEEE Computer Society Conference on Computer
Vision and Pattern Recognition, 1997, pp. 554-560.
[12] S. Lee, S. Moon, S. Lee, "Face recognition under arbitrary illumination
using illuminated exemplars," Pattern Recognition, vol. 40, pp.
1605-1620, May. 2007.
[13] K. Nishino, P. N.Belhumeur, S. K.Nayar, "Using eye reflections for face
recognition under varying illumination," In International Conference on
Computer Vision, 2005, vol. 1, pp. 519-526.
[14] A. S.Georghiades and P. N.Belhumeur, "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, pp.
643-660, Jun. 2001.
[15] Y. Wang, L. Zhang, Z. Liu, G. Hua, Z. Wen, Z. Zhang, Samaras,D., "Face
relighting from a single image under arbitrary unknown lighting
conditions," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 31, pp. 1968-1984, Nov. 2009.
[16] P. S.Wold, Kim Esbensen, "Principal component analysis,"
Chemometrics and Intelligent aboratory Systems, vol. 2, pp. 37-52, 1987.
[17] Wright J, Yang A.Y., Ganesh A., Sastry S.S., Yi Ma, "Robust face
recognition via sparse representation," IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 31, pp. 210-227, Feb. 2009.
[18] X. Xie and K. M.Lam, "An efficient illumination normalization method
for face recognition," Pattern Recognition Letters, vol. 27, pp. 609-617,
Apr. 2006.
[19] E. J.Ferwerda, M. StarkP. ShirleyJ. Ferwerda, "Photographic tome
reproduction for digital image," in Proceedings of SIGGRAPH, pp.
267-276, Jul. 2002.
[20] T. sim, S. Baker, M. Bsat, "The CMU pose, illumination, and experession
database," IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 25, pp. 1615-1618, Dec. 2003.
@article{"International Journal of Information, Control and Computer Sciences:60771", author = "Pengfei Xiong and Lei Huang and Changping Liu", title = "Face Texture Reconstruction for Illumination Variant Face Recognition", abstract = "In illumination variant face recognition, existing
methods extracting face albedo as light normalized image may lead to
loss of extensive facial details, with light template discarded. To
improve that, a novel approach for realistic facial texture
reconstruction by combining original image and albedo image is
proposed. First, light subspaces of different identities are established
from the given reference face images; then by projecting the original
and albedo image into each light subspace respectively, texture
reference images with corresponding lighting are reconstructed and
two texture subspaces are formed. According to the projections in
texture subspaces, facial texture with normal light can be synthesized.
Due to the combination of original image, facial details can be
preserved with face albedo. In addition, image partition is applied to
improve the synthesization performance. Experiments on Yale B and
CMUPIE databases demonstrate that this algorithm outperforms the
others both in image representation and in face recognition.", keywords = "texture reconstruction, illumination, face recognition,
subspaces", volume = "5", number = "5", pages = "497-7", }