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Abstract: To overcome the high computational complexity in real-time classifier design, we propose a fast classification scheme. A new measure called ‘reconstruction proportion’ is exploited to reflect the discriminant information. A novel space called the ‘reconstruction space’ is constructed according to the reconstruction proportions. A point in the reconstruction space denotes the case of a sample reconstructed using training samples. This is used to search for an optimal mapping from the conventional sample space to the reconstruction space. When the projection from the sample space to the reconstruction space is obtained, a new sample after mapping to the new discriminant space would be classified quickly according to the reconstruction proportions in the reconstruction space. This projection technique results in a diversion of time-consuming calculations from the classification stage to the training stage. Though training time is prolonged, it is advantageous in that classification problems such as identification can be solved in real time. Experimental results on the ORL, Yale, YaleB, and CMU PIE face databases showed that the proposed fast classification scheme greatly outperforms conventional classifiers in classification accuracy and efficiency.

[1]Abate, A.F., Nappi, M., Riccio, D., Sabatino, G., 2007. 2D and 3D face recognition: a survey. Pattern Recogn. Lett., 28(14):1885-1906.

[2]Athitsos, V., Alon, J., Sclaroff, S., Kollios, G., 2008. Boostmap: an embedding method for efficient nearest neighbor retrieval. IEEE Trans. Pattern Anal. Mach. Intell., 30(1):89-104.

[3]Baraniuk, R.G., 2007. Compressive sensing. IEEE Signal Process. Mag., 24(4):118-121.

[4]Bax, E., 2000. Validation of nearest neighbor classifiers. IEEE Trans. Inf. Theory, 46(7):2746-2752.

[5]Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J., 1997. Eigenfaces vs. Fisher-faces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell., 19(7):711-720.

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

[7]Candes, E.J., Wakin, M.B., 2008. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2):21-30.

[8]Cevikalp, H., Triggs, B., Polikar, R., 2008. Nearest Hyperdisk Methods for High-Dimensional Classification. Proc. 25th Int. Conf. on Machine Learning, p.120-127.

[9]Chen, C.J., 2010. Development of a Neural Classifier with Genetic Algorithm for Structural Vibration Suppression. Proc. Int. Conf. on Machine Learning and Cybernetics, p.2788-2795.

[10]Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inf. Theory, 52(4):1289-1306.

[11]Duda, R., Hart, P., Stork, D., 2001. Pattern Classification (2nd Ed.). John Wiley & Sons, New York.

[12]He, X.F., Niyogi, P., 2003. Locality Preserving Projections. Advances in Neural Information Processing System 16.

[13]He, X.F., Cai, D., Yan, S.C., Zhang, H.J., 2005. Neighborhood Preserving Embedding. IEEE Int. Conf. on Computer Vision, p.1208-1213.

[14]Ho, J., Yang, M., Lim, J., Lee, K., Kriegman, D., 2003. Clustering Appearances of Objects under Varying Illumination Conditions. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.11-18.

[15]Jain, A., Chandrasekaran, B., 1982. Dimensionality and Samples Size Considerations in Pattern Recognition Practice. Classification Pattern Recognition and Reduction of Dimensionality p.835-855.

[16]Jiang, X.D., Mandal, B., Kot, A., 2008. Eigenfeature regularization and extraction in face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 30(3):383-394.

[17]Keller, J.M., Gray, M.R., Givens, J.A., 1985. A fuzzy K-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern., 15(4):580-585.

[18]Li, H.F., Jiang, T., Zhang, K.S., 2006. Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neur. Networks, 17(1):157-165.

[19]Li, S.Z., Lu, J., 1999. Face recognition using the nearest feature line method. IEEE Trans. Neur. Networks, 10(2):439-443.

[20]Liu, Y.G., Sam, S.Z., Li, C.G., You, Z.S., 2011. K-NS: a classifier by the distance to the nearest subspace. IEEE Trans. Neur. Networks, 22(8):1256-1268.

[21]Meo, R., Bachar, D., Ienco, D., 2012. LODE: a distance-based classifier built on ensembles of positive and negative observations. Pattern Recogn., 45(4):1409-1425.

[22]Mullin, M., Sukthankar, R., 2000. Complete Cross-Validation for Nearest Neighbor Classifiers. Proc. Int. Conf. on Machine Learning, p.639-646.

[23]Psaltis, D., Snapp, R., Venkatesh, S., 1994. On the finite sample performance of the nearest neighbor classifier. IEEE Trans. Inf. Theory, 40(3):820-837.

[24]Qiao, L.S., Chen, S.C., Tan, X.Y., 2010. Sparsity preserving projections with applications to face recognition. Pattern Recogn., 43(1):331-341.

[25]Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326.

[26]Singh, C., Walia, E., Mittal, N., 2012. Robust two-stage face recognition approach using global and local features. Vis. Comput., 28(11):1085-1098.

[27]Stefan, A., Athitsos, V., Yuan, Q., Sclaroff, S., 2009. Reducing Jointboost-Based Multiclass Classification to Proximity Search. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.589-596.

[28]Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science, 29(5500):2319-2323.

[29]Turk, M., Pentland, A., 1991. Eigenface for recognition. J. Cogn. Neurosci., 3(1):71-86.

[30]Vincent, P., Bengio, Y., 2002. K-Local Hperplane and Convex Distance Nearest Neighbor Algorithms. Advances in Neural Information Processing Systems, p.985-992.

[31]Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y., 2009. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell., 31(2):210-227.

[32]Zhang, B., Srihari, S.N., 2004. Fast k-nearest neighbor classification using cluster-based trees. IEEE Trans. Pattern Anal. Mach. Intell., 26(4):525-528.

[33]Zhang, D.Q., Chen, S.C., Zhou, Z.H., 2006. Learning the kernel parameters in kernel minimum distance classifier. Pattern Recogn., 39(1):133-135.

[34]Zhang, P., Zhu, X.Q., Tan, J.L., Guo, L., 2010. Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams. Proc. IEEE Int. Conf. on Data Mining, p.1175-1180.

[35]Zhang, X.Z., Gao, Y.S., 2009. Face recognition across pose: a review. Pattern Recogn., 42(11):2876-2896.

[36]Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J., 2003. Face recognition: a literature survey. ACM Comput. Surv., 35(4):399-458.