Abstract
This paper presents a novel face recognition method based on cascade Linear Discriminant Analysis (LDA) of the component-based face representation. In the proposed method, a face image is represented as four components with overlap at the neighboring area rather than a whole face patch. Firstly, LDA is conducted on the principal components of each component individually to extract component discriminant features. Then, these features are further concatenated to undergo another LDA to extract the final face descriptor, which actually have assigned different weights to different component features. Our experiments on the FERET face database have illustrated the effectiveness of the proposed method compared with the traditional Fisherface method both for face recognition and verification.
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
Samal, P.A.I.: Automatic Recognition and Analysis of Human Faces and Facial Expressions: A Survey. Pattern Recognition 25(1), 65–77 (1992)
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and Machine Recognition of faces: A survey. Proc. of the IEEE 83(5), 705–740 (1995)
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey, Technical Report, CS-TR4167, University of Maryland, Revised, CS-TR4167R (2000)
Brunelli, R., Poggio, T.: Face Recognition. Features versus Template, TPAMI 15(10), 1042–1052 (1993)
Heisele, B., Ho, P., Poggio, T.: Face Recognition with Support Vector Machine: Global versus Component-based Approach. In: International Conference on Computer Vision (2001)
Sirovitch, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America A 2, 519–524 (1987)
Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)
Belhumer, P., Hespanha, P., Kriegman, D.: Eigenfaecs vs fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)
Fleming, M., Cottrell, G.: Categorization of faces using unsupervised feature extraction. In: Proc. IEEE IJCNN International Joint Conference on Neural Networks, pp. 65–70 (1990)
Moghaddam, B., Wahid, W., Pentland, A.: Beyond eigenfaces: probabilistic matching for face recognition. In: Proc. IEEE International Conference on Automatic Face and Gesture Recognition, pp. 30–35 (1998)
Lanitis, A., Taylor, C., Cootes, T.: Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 743–756 (1997)
Penev, P., Atick, J.: Local Feature Analysis: A General Statistical Theory for Object Representation. Network: Computation in Neural Systems 7, 477–500 (1996)
Zhang, J., Yan, Y., Lades, M.: Face Recognition: Eigenface, Elastic Matching and Neural Nets. Proceedings of the IEEE 85(9), 1422–1435 (1997)
Wiskott, L., Fellous, J.M., Kruger, N., Malsburg, C.V.D.: Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. On PAMI 19(7), 775–779 (1997)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience, USA
Kim, T.-K., Kim, H., Hwang, W., Kee, S.C., Lee, J.H.: Component-based LDA Face Descriptor for Image Retrieval. In: BMVC (2002)
Rizvi, S., Phillips, P.J., Moon, H.: The FERET Verification Testing Protocol for Face Recognition Algorithm, Image and Vision Computing J. (to appear)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET Evaluation Methodology for Face-Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Jones, M.J., Viola, P.: Face Recognition Using Boosted Local Features, Technical Report, MITSUBISHI ELECTRIC RESEARCH LABORATORIES, TR2003–25 April (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, W., Shan, S., Gao, W., Chang, Y., Cao, B. (2004). Component-Based Cascade Linear Discriminant Analysis for Face Recognition. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-30548-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24029-7
Online ISBN: 978-3-540-30548-4
eBook Packages: Computer ScienceComputer Science (R0)