Abstract
In human face recognition, different facial regions have different degrees of importance, and exploiting such information would hopefully improve the accuracy of the recognition system. A novel method is therefore proposed in this paper to automatically select the facial regions that are important for recognition. Unlike most of previous attempts, the selection is based on the facial appearance of individual subjects, rather than the appearance of all subjects. Hence the recognition process is class-specific. Experiments on the FERET face database show that the proposed methods can automatically and correctly identify those supposed important local features for recognition and thus are much beneficial to improve the recognition accuracy of the recognition system even under the condition of only one single training sample per person.
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.
Similar content being viewed by others
References
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Survey 35(4), 399–458 (2003)
Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE TPAMI 15(10), 1042–1062 (1993)
Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proc. of the IEEE Inter. Conf. CVPR, Seattle, WA, pp. 84–91 (1994)
Tan, X.Y., Chen, S.C., Zhou, Z.-H., Zhang, F.: Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft kNN ensemble. IEEE Transactions on Neural Networks (in press)
Chen, S.C., Liu, J., Zhou, Z.-H.: Making FLDA applicable to face recognition with one sample per person. Patt. Recog. 37(7), 1553–1555 (2004)
Chen, S.C., Zhang, D.Q., Zhou, Z.-H.: Enhanced (PC)2A for face recognition with one training image per person. Patt. Recog. Lett. 25, 1173–1181 (2004)
Martinez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE TPAMI 25(6), 748–763 (2002)
Kohonen, T.: Self-Organizing Map, 2nd edn. Springer, Berlin (1997)
Grossman, D.A., Frieder, O.: Information Retrieval: Algorithms and Heuristics. Kluwer, Boston (1998)
Singh, S., Singh, M., Markou, M.: Feature Selection for face Recognition based on Data Partitioning. In: Proc. 15th Int. Conf. Patt. Recog., ICPR 2002, August 11-15 (2002)
Phillips, P.J., Wechsler, H., et al.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)
Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE TPAMI 19(7), 711–720 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, X., Liu, J., Chen, S., Zhang, F. (2005). Weighted SOM-Face: Selecting Local Features for Recognition from Individual Face Image. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_46
Download citation
DOI: https://doi.org/10.1007/11508069_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
Online ISBN: 978-3-540-31693-0
eBook Packages: Computer ScienceComputer Science (R0)