Face recognition based on manifold learning and Rényi entropy

DOI: 10.4236/ns.2010.21007   PDF   HTML     5,111 Downloads   9,809 Views   Citations


Though manifold learning has been success-fully applied in wide areas, such as data visu-alization, dimension reduction and speech rec-ognition; few researches have been done with the combination of the information theory and the geometrical learning. In this paper, we carry out a bold exploration in this field, raise a new approach on face recognition, the intrinsic α-Rényi entropy of the face image attained from manifold learning is used as the characteristic measure during recognition. The new algorithm is tested on ORL face database, and the ex-periments obtain the satisfying results.

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Cao, W. and Li, N. (2010) Face recognition based on manifold learning and Rényi entropy. Natural Science, 2, 49-53. doi: 10.4236/ns.2010.21007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Lin, T. (2005) Machine perception of human faces: a grand challenge. The Korea Foundations for Advanced Studies (KFAS).
[2] Rowley, H.A., Baluja, S. and Kanade, T. (1998) Neural network-based face detection [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 20(1), 23-38.
[3] Feraud, R., Bernier, O.J., Villet, J.E. and Collobert, M. (2001) A fast and accurate face detector based on neural networks [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 22(1), 42-53.
[4] Garcia, C. and Delakis, M. (2004) Convolutional face finder: a neural architecture for fast and robust face de-tection [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 26(11), 1408-1423.
[5] Osuna, E., Freund, R. and Girosi F. (1997) Training sup-port vector machines: an application to face detection [J]. Proc, IEEE Conf Computer Vision and Pattern Recogni-tion, 130-136.
[6] Phillips, P.J. (1998) Support vector machines applied to face recognition [J]. Adv. Neural Inform. Process. Syst. 11, 803-809.
[7] Viola, P. and Jones, M. J. (2004) Robust real-time face detection [J]. Int. J. Computer Vision, 57(2).
[8] Li, S.Z. and Zhang, Z. (2004) Float boost learning and statistical face detection [J]. IEEE Trans Pattern Anal. Mach. Intel., 26(9), 1112-1123.
[9] Kirby, M. and Sirovic, L. (1990) Application of the kar-hunen-loeve procedure for the characterization of human faces [J]. IEEE Trans Pattern Anal. Mach. Intel., 12(1), 103-108.
[10] Turk, M. and Pentland, A. (1991) Eigenfaces for Recog-nition [J]. J. Cogn. Neurosci, 3, 72-86.
[11] Belhumeur, P.N., Hespanha, J.P. and Kriegman, D.J. (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection [J]. IEEE Transactions on Pat-tern Analysis and Machine Intelligence, 19(7), 711- 720.
[12] Kim, T.K. and Kittler, J. (2005) Locally linear discrimi-nant analysis for multimodally distributed classes for face recognition with a single model image [J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 27(3), 318-327.
[13] Tenenbaum, J.B., de Silva, V. and Langford, J.C. (2000) A global geometric framework for nonlinear dimension-ality reduction [J] Science, 290, 2319-2323.
[14] Roweis, S.T. and Saul L.K. (2000) Nonlinear dimension-ality reduction by locally linear embedding [J]. Science, 290, 2323-2326.
[15] Belkin, M. and Niyogi, P. (2003) Laplacian eigenmaps for dimensionality reduction and data representation [J]. Neural Computation, 15(6), 1373-1396.
[16] He, X., Yan, S., Hu, Y., Niyogi, P., and Zhang, H. (2005) Face recognition using laplacianfaces [J]. IEEE Transac-tions on Pattern Analysis and Machine Intelligence, 27(3), 328-340.
[17] Costa, J.A. and Hero, A.O. (2003) Entropic graphs for manifold learning [J]. In the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 316- 320.
[18] Costa, J.A. and Hero, A.O. (2004) Manifold learning using k-nearest neighbor graphs [J]. Proc. of IEEE Int. Conf. on Acoust. Speech and Signal Processing. Mont-real, Canada.
[19] ORL, Face Database, AT&T Laboratories Cambridge. [DB/OL]. http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip.

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