Local uncorrelated local discriminant embedding for face recognition

Abstract

The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.

Keywords

Feature extraction / Local discriminant embedding / Local uncorrelated criterion / Face recognition

References

[1] Belhumeur, P.N., Hespanha, J.P., Kriegman, D., 1997. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intell., 19(7):711–720. http://dx.doi.org/10.1109/34.598228

[2] Belkin, M., Niyogi, P., 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neur. Comput., 15(6):1373–1396.

[3] Chen, H.T., Chang, H.W., Liu, T.L., 2005. Local discriminant embedding and its variants. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.846–853. http://dx.doi.org/10.1109/CVPR.2005.216

[4] Chen, Y., Zheng, W.S., Xu, X.H., , 2013. Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition. Neur. Netw., 42:28–43. http://dx.doi.org/10.1016/j.neunet.2013.01.009

[5] Fan, Z.Z., Xu, Y., Zhang, D., 2011. Local linear discriminant analysis framework using sample neighbors. IEEE Trans. Neur. Netw., 22(7):1119–1132. http://dx.doi.org/10.1109/TNN.2011.2152852

[6] He, X.F., Niyogi, P., 2003. Locality preserving projections. Proc. Advances in Neural Information Processing Systems, p.327–334.

[7] Jin, Z., Yang, J.Y., Hu, Z.S., , 2001. Face recognition based on the uncorrelated discriminant transformation. Patt. Recog., 34(7):1405–1416. http://dx.doi.org/10.1016/S0031-3203(00)00084-4

[8] Jing, X.Y., Zhang, D., Jin, Z., 2003. UODV: improved algorithm and generalized theory. Patt. Recog., 36(11):2593–2602. http://dx.doi.org/10.1016/S0031-3203(03)00177-8

[9] Jing, X.Y., Li, S., Zhang, D., , 2011. Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms. Proc. 18th IEEE Int. Conf. on Image Processing, p.3049–3052. http://dx.doi.org/10.1109/ICIP.2011.6116307

[10] Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323–2326. http://dx.doi.org/10.1126/science.290.5500.2323

[11] Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319–2323. http://dx.doi.org/10.1126/science.290.5500.2319

[12] Turk, M., Pentland, A., 1991. Eigenfaces for recognition. J. Cogn. Neurosci., 3(1):71–86.

[13] Wong, W.K., Zhao, H.T., 2012. Supervised optimal locality preserving projection. Patt. Recog., 45(1):186–197. http://dx.doi.org/10.1016/j.patcog.2011.05.014

[14] Yan, S.C., Xu, D., Zhang, B.Y., , 2007. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Patt. Anal. Mach. Intell., 29(1):40–51. http://dx.doi.org/10.1109/TPAMI.2007.250598