Abstract
We propose the Facial Trait Code (FTC) to encode human facial images. The proposed FTC is motivated by the discovery of the basic types of local facial features, called facial trait bases, which can be extracted from a large number of faces. In addition, the fusion of these facial trait bases can accurately capture the appearance of a face. Extraction of the facial trait bases involves clustering and boosting approaches, leading to the best discrimination of the human faces. The extracted facial trait bases are symbolized and make up the n-ary facial trait codes. A given face can be then encoded at the patches specified by the traits to render an n-ary facial trait code with each symbol in its codeword corresponding to the closest trait base. We applied FTC to a typical face identification problem, and it yielded satisfactory results under different illumination conditions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Heisele, B., Ho, P., Wu, J., Poggio, T.: Face recognition: component-based versus global approaches. CVIU 91, 6–12 (2003)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: CVPR, pp. 84–91 (1994)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 511–518 (2001)
Xie, C., Kumar, B.V.: Face class code based feature extraction for face recognition. In: Fourth IEEE Workshop on Publication Automatic Identification Advanced Technologies, pp. 257–262 (2005)
Figueiredo, M., Jain, A.: unsupervised learning of finite mixture models. PAMI 24, 381–396 (2002)
Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)
Grother, P.J., Micheals, R.J., Phillips, P.J.: Face recognition vendor test 2002 performance metrics. In: AVBPA (2003)
Martinez, A., Benavente, R.: The ar face database. Technical Report 24, CVC (1998)
Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: Xm2vtsdb: The extended m2vts database. In: AVBPA (1999)
Zuiderveld, K.: Contrast limited adaptive histogram equalization, pp. 474–485 (1994)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. PAMI 19, 711–720 (1997)
Guo, G., Li, S.Z., Chan, K.: Face recognition by support vector machines. In: Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196–201 (2000)
Guruswami, V., Sahai, A.: Multiclass learning, boosting, and error-correcting codes. In: Proceedings of the twelfth annual conference on Computational learning theory, pp. 145–155 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, PH., Hsu, GS., Chen, T., Hung, YP. (2008). Facial Trait Code and Its Application to Face Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89646-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-89646-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89645-6
Online ISBN: 978-3-540-89646-3
eBook Packages: Computer ScienceComputer Science (R0)