[1] D. Chen, X. Cao, F. Wen, and J. Sun, “Blessing of dimensionality: Highdimensionalfeature and its efficient compression for face verification,”in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.,Jun. 2013, pp. 3025–3032.

[2] D. G. Lowe, “Object recognition from local scale-invariant features,” inProc. IEEE Int. Conf. Comput. Vis., Sep. 1999, pp. 1150–1157.

[3] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face recognition with localbinary patterns,” in Proc. ECCV, 2004, pp. 469–481.

[4] N. Dalal and B. Triggs, “Histograms of oriented gradients for humandetection,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. PatternRecognit., Jun. 2005, pp. 886–893.

[5] K. Simonyan, O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Fishervector faces in the wild,” in Proc. BMVC, 2013, pp. 8.1–8.12.

[6] Z. Cao, Q. Yin, X. Tang, and J. Sun, “Face recognition with learningbaseddescriptor,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Jun. 2010, pp. 2707–2714.

[7] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfacesvs. Fisherfaces: Recognition using class specific linear projection,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720,Jul. 1997.

[8] B.-K. Bao, G. Liu, R. Hong, S. Yan, and C. Xu, “General subspacelearning with corrupted training data via graph embedding,” IEEE Trans.Image

[1] D. Chen, X. Cao, F. Wen, and J. Sun, “Blessing of dimensionality: Highdimensionalfeature and its efficient compression for face verification,”in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.,Jun. 2013, pp. 3025–3032.

[2] D. G. Lowe, “Object recognition from local scale-invariant features,” inProc. IEEE Int. Conf. Comput. Vis., Sep. 1999, pp. 1150–1157.

[3] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face recognition with localbinary patterns,” in Proc. ECCV, 2004, pp. 469–481.

[4] N. Dalal and B. Triggs, “Histograms of oriented gradients for humandetection,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. PatternRecognit., Jun. 2005, pp. 886–893.

[5] K. Simonyan, O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Fishervector faces in the wild,” in Proc. BMVC, 2013, pp. 8.1–8.12.

[6] Z. Cao, Q. Yin, X. Tang, and J. Sun, “Face recognition with learningbaseddescriptor,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Jun. 2010, pp. 2707–2714.

[7] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfacesvs. Fisherfaces: Recognition using class specific linear projection,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720,Jul. 1997.

[8] B.-K. Bao, G. Liu, R. Hong, S. Yan, and C. Xu, “General subspacelearning with corrupted training data via graph embedding,” IEEE Trans.Image Process., vol. 22, no. 11, pp. 4380–4393, Nov. 2013.

[9] Z. Lai, Y. Xu, J. Yang, J. Tang, and D. Zhang, “Sparse tensordiscriminant analysis,” IEEE Trans. Image Process., vol. 22, no. 10,pp. 3904–3915, Oct. 2013. [10] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “Face recognitionusing kernel direct discriminant analysis algorithms,” IEEE Trans.Neural Netw., vol. 14, no. 1, pp. 117–126, Jan. 2003. [11] M.-H. Yang, “Kernel eigenfaces vs. kernel Fisherfaces: Face recognitionusing kernel methods,” in Proc. 5th Int. Conf. Face Gesture Recognit.,May 2002, pp. 215–220 .[12] P. Li, Y. Fu, U. Mohammed, J. H. Elder, and S. J. D. Prince, “Probabilistic models for inference about identity,” IEEE Trans. Pattern Anal.Mach. Intell., vol. 34, no. 1, pp. 144–157, Jan. 2012. [13] R. Singh, M. Vatsa, A. Ross, and A. Noore, “Online learning inbiometrics: A case study in face classifier update,” in Proc. BTAS,Sep. 2009, pp. 1– 6. [14] F. Cardinaux, C. Sanderson, and S. Bengio, “Face verification usingadapted generative models,” inProc. IEEE Int. Conf. Autom. FaceGesture Recognit., May 2004, pp. 825–830. [15] U. Park, Y. Tong, and A. K. Jain, “Ageinvariant face recognition,”IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 5, pp. 947– 954,May 2010.

[16] J.-X. Du, C.-M. Zhai, and Y.-Q. Ye, “Face aging simulation based onNMF algorithm with sparseness constraints,” Neurocmputing, vol. 116,no. 20, pp. 250–259, 2012. [17] C. Ma, X. Yang, C. Zhang, X. Ruan, and M.- H. Yang, “Sketch retrievalvia dense stroke features,” in Proc. BMVC, 2013, pp. 65.1–65.11.

[18] T. Mäenpää and M. Pietikäinen, “Multi-scale binary patterns for textureanalysis,” in Proc. SCIA, 2003, pp. 885–892.

[19] M. A. Turk and A. P. Pentland, “Facerecognition using eigenfaces,”in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.,Jun. 1991, pp. 586–591.

[20] C. Liu, “Gabor-based kernel PCA with fractional power polynomialmodels for face recognition,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 26, no. 5, pp. 572–581, May 2004.

[21] B. F. Klare, Z. Li, and A. K. Jain, “Matching forensic sketches to mugshot photos,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 3,pp. 639– 646, Mar. 2011. [22] X. Wang and X. Tang, “A unified framework for subspace facerecognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9,pp. 1222–1228, Sep. 2004.

[23] G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, “Labeledfaces in the wild: A database for studying face recognition in unconstrainedenvironments,” Univ. Massachusetts Amherst, Amherst, MA,USA, Tech. Rep. 07-49, Oct. 2007.

[24] K. Ricanek and T. Tesafaye, “MORPH: A longitudinal image databaseof normal adult ageprogression,” in Proc. 7th Int. Conf. Autom. FaceGesture Recognit., Apr. 2006, pp. 341–345