Probabilistic Bayesian Framework for Infrared Face Recognition
Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA and LDA. In this work, we introduce a probabilistic Bayesian framework for face recognition in the infrared spectrum. In the infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. Bayesian approaches permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. This framework is compared with classical linear techniques. Non linear techniques we developed recently for infrared face recognition are also presented and compared to the Bayesian face recognition framework. A new approach for infrared face extraction based on SVM is introduced. Experimental results show that the Bayesian technique is promising and lead to interesting results in the infrared spectrum when a sufficient number of face images is used in an intrapersonal learning process.
[1] A.A. Ross, K. Nandakumar, and A.K. Jain, Handbook of
Multibiometrics, Springer, 2006.
[2] A.K. Jain, P. Flynn, and A.A. Ross, Handbook of Biometrics, Springer,
2007.
[3] A. Bronstein, M. Bronstein, and R. Kimmel., "Three-dimensional face
recognition", International Journal of Computer Vision, vol. 64, pp. 5-
30, 2005.
[4] S. Kong, J. Heo., B. Abidi, J. Paik, and M. Abidi, "Recent advances in
visual and infrared face recognition: a review", Computer Vision &
Image Understanding, vol. 97, pp. 103-135, 2005.
[5] M.A. Akhloufi, A. Bendada, and J.C. Batsale, "State of the art in
Infrared face recognition", QIRT Journal, vol.5, n.1, pp.3-26, June
2008.
[6] P. Buddharaju, I.T. Pavlidis and P. Tsiamyrtzis, "Pose-Invariant
Physiological Face Recognition in the Thermal Infrared Spectrum",
IEEE Conference on Computer Vision and Pattern Recognition
Workshop, New York , pp. 53-53, 2006.
[7] M.A. Akhloufi, A. Bendada, "Thermal Faceprint: A new thermal face
signature extraction for infrared face recognition", Proceedings of the
5th Canadian Conference on Computer and Robot Vision (CRV 2008),
Windsor, ON, Canada, May 2008.
[8] M.A. Akhloufi, A. Bendada, "Infrared Face Recognition Using Distance
Transforms", Proceedings of the 5th International Conference on Image
and Vision Computing (ICIVC 2008), Vol.30, pp. 160-163, Paris,
France, July 04-06, 2008.
[9] M. A. Akhloufi, A. Bendada, and J.C. Batsale, "Multispectral face
recognition using non linear dimensionality reduction", Proceedings of
SPIE Visual Information Processing XVIII conference, Volume 7341,
SPIE Defense, Security and Sensing, 7341-19, Orlando, Florida, USA,
2009.
[10] B. Moghaddam, C. Nastar, and A. Pentland, "Bayesian face recognition
using deformable intensity surfaces", IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR'96),
San Francisco, CA, USA, pp. 638-645, 1996.
[11] B. Moghaddam, T. Jebara, and A. Pentland, "Bayesian Face
Recognition", Pattern Recognition, Vol. 33, Issue 11, pp. 1771-1782,
2000.
[12] Equinox, "Multimodal face database",
http://www.equinoxsensors.com/products/HID.html, last visited August
2009.
[13] Journaux, L., Foucherot, I. and Gouton, P., "Operational Comparison of
Dimensionality Reduction Techniques applied on Multispectral Satellite
Images," Proc. 4th Int. Conf. Signal-Image Technology & Internet-
Based Syst., 476-484 (2006).
[14] Yang, M., Ahuja, N., and Kriegman, D., "Face Recognition Using
Kernel Eigenfaces", Proc. of IEEE ICIP, 37-40 (2000).
[15] Liu, Q., Huang, R., Lu, H., and Ma, S., "Kernel-based Optimized
Feature Vectors Selection and Discriminant Analysis for Face
Recognition", Proc of ICPR, 362-365 (2002).
[16] Pang, Y., Zhang, L., Liu, Z., Yu, N., and Li, H., "Neighborhood
Preserving Projections (NPP): A Novel Linear Dimension Reduction
Method", Proc. of International Conference on Intelligent Computing
(ICIC 2005), 23-26 (2005).
[17] He, X., Yan, S., Hu, Y., and Zhang, H., "Learning a Locality Preserving
Subspace for Visual Recognition", Proc.of the IEEE Int. Conf. on
Comp. Vision (ICCV 2003), 385-393 (2003).
[18] Niyogi, P., "Laplacian Eigenmaps for Dimensionality Reduction and
Data Representation", Neural Computation, 15, 1373-1396 (2003).
[1] A.A. Ross, K. Nandakumar, and A.K. Jain, Handbook of
Multibiometrics, Springer, 2006.
[2] A.K. Jain, P. Flynn, and A.A. Ross, Handbook of Biometrics, Springer,
2007.
[3] A. Bronstein, M. Bronstein, and R. Kimmel., "Three-dimensional face
recognition", International Journal of Computer Vision, vol. 64, pp. 5-
30, 2005.
[4] S. Kong, J. Heo., B. Abidi, J. Paik, and M. Abidi, "Recent advances in
visual and infrared face recognition: a review", Computer Vision &
Image Understanding, vol. 97, pp. 103-135, 2005.
[5] M.A. Akhloufi, A. Bendada, and J.C. Batsale, "State of the art in
Infrared face recognition", QIRT Journal, vol.5, n.1, pp.3-26, June
2008.
[6] P. Buddharaju, I.T. Pavlidis and P. Tsiamyrtzis, "Pose-Invariant
Physiological Face Recognition in the Thermal Infrared Spectrum",
IEEE Conference on Computer Vision and Pattern Recognition
Workshop, New York , pp. 53-53, 2006.
[7] M.A. Akhloufi, A. Bendada, "Thermal Faceprint: A new thermal face
signature extraction for infrared face recognition", Proceedings of the
5th Canadian Conference on Computer and Robot Vision (CRV 2008),
Windsor, ON, Canada, May 2008.
[8] M.A. Akhloufi, A. Bendada, "Infrared Face Recognition Using Distance
Transforms", Proceedings of the 5th International Conference on Image
and Vision Computing (ICIVC 2008), Vol.30, pp. 160-163, Paris,
France, July 04-06, 2008.
[9] M. A. Akhloufi, A. Bendada, and J.C. Batsale, "Multispectral face
recognition using non linear dimensionality reduction", Proceedings of
SPIE Visual Information Processing XVIII conference, Volume 7341,
SPIE Defense, Security and Sensing, 7341-19, Orlando, Florida, USA,
2009.
[10] B. Moghaddam, C. Nastar, and A. Pentland, "Bayesian face recognition
using deformable intensity surfaces", IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR'96),
San Francisco, CA, USA, pp. 638-645, 1996.
[11] B. Moghaddam, T. Jebara, and A. Pentland, "Bayesian Face
Recognition", Pattern Recognition, Vol. 33, Issue 11, pp. 1771-1782,
2000.
[12] Equinox, "Multimodal face database",
http://www.equinoxsensors.com/products/HID.html, last visited August
2009.
[13] Journaux, L., Foucherot, I. and Gouton, P., "Operational Comparison of
Dimensionality Reduction Techniques applied on Multispectral Satellite
Images," Proc. 4th Int. Conf. Signal-Image Technology & Internet-
Based Syst., 476-484 (2006).
[14] Yang, M., Ahuja, N., and Kriegman, D., "Face Recognition Using
Kernel Eigenfaces", Proc. of IEEE ICIP, 37-40 (2000).
[15] Liu, Q., Huang, R., Lu, H., and Ma, S., "Kernel-based Optimized
Feature Vectors Selection and Discriminant Analysis for Face
Recognition", Proc of ICPR, 362-365 (2002).
[16] Pang, Y., Zhang, L., Liu, Z., Yu, N., and Li, H., "Neighborhood
Preserving Projections (NPP): A Novel Linear Dimension Reduction
Method", Proc. of International Conference on Intelligent Computing
(ICIC 2005), 23-26 (2005).
[17] He, X., Yan, S., Hu, Y., and Zhang, H., "Learning a Locality Preserving
Subspace for Visual Recognition", Proc.of the IEEE Int. Conf. on
Comp. Vision (ICCV 2003), 385-393 (2003).
[18] Niyogi, P., "Laplacian Eigenmaps for Dimensionality Reduction and
Data Representation", Neural Computation, 15, 1373-1396 (2003).
@article{"International Journal of Information, Control and Computer Sciences:61863", author = "Moulay A. Akhloufi and Abdelhakim Bendada", title = "Probabilistic Bayesian Framework for Infrared Face Recognition", abstract = "Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA and LDA. In this work, we introduce a probabilistic Bayesian framework for face recognition in the infrared spectrum. In the infrared spectrum, variations can occur between face images of the same individual due to pose, metabolic, time changes, etc. Bayesian approaches permit to reduce intrapersonal variation, thus making them very interesting for infrared face recognition. This framework is compared with classical linear techniques. Non linear techniques we developed recently for infrared face recognition are also presented and compared to the Bayesian face recognition framework. A new approach for infrared face extraction based on SVM is introduced. Experimental results show that the Bayesian technique is promising and lead to interesting results in the infrared spectrum when a sufficient number of face images is used in an intrapersonal learning process.
", keywords = "Face recognition, biometrics, probabilistic imageprocessing, infrared imaging.", volume = "3", number = "12", pages = "2918-5", }
