MULTI-DISCRIMINANT CLASSIFICATION ALGORITHM FOR FACE VERIFICATION

Cheng-Ho Huang, Jhing-Fa Wang

Abstract

Linear discriminant analysis (LDA) is a conventional approach for face verification. For computing large amounts of data collected for a given face verification system, this study proposes a multi-discriminant classification algorithm to classify and verify voluminous facial images. In the training phase, the algorithm extracts all discriminant features of the training data, and classifies them as the clients’ multi-discriminant sets. The algorithm verifies a claim to the client’s multi-discriminant set, and then determines whether the claimant is the client. Comparative results demonstrate that the proposed algorithm reduces the false acceptance rate in face verification.

References

  1. Belhumeur, P. N., Hespanha, J. P., and Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(7):711- 720.
  2. Bowyer, K. and Flynn, P. (2003). University of notre dame biometrics database-b. http://www.nd.edu/ cvrl/UNDBiometricsDatabase.html.
  3. Howland, P. and Park, H. (2004). Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(8):995-1006.
  4. Lin, D., Yan, S., and Tang, X. (2005). Feedback-based dynamic generalized lda for face recognition. Int. Conf. on Image Processing, 2:922-925.
  5. Liu, C. and Wechsler, H. (1998). Enhanced fisher linear discriminant models for face recognition. Proc. of the 14th Int. Conf. on Pattern Recognition, 2:1368.
  6. Loog, M., Duin, R. P. W., and Haeb-Umbach, R. (2001). Multiclass linear dimension reduction by weighted pairwise fisher criteria. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(7):762-766.
  7. Martinez, A. M. and Kak, A. C. (2001). Pca versus lda. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(2):228-233.
  8. Messer, K., Matas, J., Kittler, J., Luettin, J., and Maitre, G. (1999). XM2VTSDB: The Extended M2VTS Database. Proc. 2nd International Conference on Audio- and Video-based Biometric Person Authentication.
  9. Rizvi, S. A., Phillips, P. J., and Moon, H. (1998). The feret verification testing protocol for face recognition algorithms. Proc. of the 3rd. Int. Conf. on Face & Gesture Recognition, page 48.
  10. Turk, M. A. and Pentland, A. P. (1991). Face recognition using eigenfaces. Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 586-591.
  11. Wang, X. and Tang, X. (2004). A unified framework for subspace face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(9):1222-1228.
Download


Paper Citation


in Harvard Style

Huang C. and Wang J. (2008). MULTI-DISCRIMINANT CLASSIFICATION ALGORITHM FOR FACE VERIFICATION . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 299-304. DOI: 10.5220/0001082202990304


in Bibtex Style

@conference{visapp08,
author={Cheng-Ho Huang and Jhing-Fa Wang},
title={MULTI-DISCRIMINANT CLASSIFICATION ALGORITHM FOR FACE VERIFICATION},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={299-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001082202990304},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - MULTI-DISCRIMINANT CLASSIFICATION ALGORITHM FOR FACE VERIFICATION
SN - 978-989-8111-21-0
AU - Huang C.
AU - Wang J.
PY - 2008
SP - 299
EP - 304
DO - 10.5220/0001082202990304