 B. Klare and A. K. Jain, “Heterogeneous face recognition using kernel prototype similarities,” IEEE Trans. Pattern Anal. Mach. Intel., vol. 35,no. 6, pp. 1410–1422, Jun. 2013.
 Z. Lei, S. Liao, A. K. Jain, and S. Z. Li, “Coupled discriminant analysis for heterogeneous face recognition,” IEEE Trans. Inf. Forensics Security,vol. 7, no. 6, pp. 1707–1716, Dec. 2012.
 S. Liao, D. Yi, Z. Lei, R. Qin, and S. Li, “Heterogeneous face recognition from local structures of normalized appearance,” in Proc. 3rd ICB, pp. 209–218, 2009
 Y. Fu, S. Yan, and T. S. Huang, “Correlation metric for generalized feature extraction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 12, pp. 2229–2235, Dec. 2008.
 T. Ahonen, E. Rahtu, V. Ojansivu, and J. Heikkila¨ , “Recognition of Blurred Faces Using Local Phase Quantization,” Proc. Int’l Conf. Pattern Recognition, pp. 14, 2008.
 S. A. Billings, H. Wei, and M. A. Balikhin, “Generalized multiscale radial basis function networks,” Neural Netw., vol. 20, no. 10, pp. 1081–1094, Dec. 2007.
 T. Ahonen, A. Hadid, and M. Pietika¨inen, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 20372041, Dec. 2006.
 J. Yang, A. F. Franji, J. Y. Yang, D. Zhang, and Z. Jin, “KPCA plus LDA:A complete kernel fisher discriminant framework for feature extraction and recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 230–244, Feb. 2005.
 R. Neruda and P. Kudová, “Learning methods for radial basis function networks,” Future Generat. Comput. Syst., vol. 21, no. 7, pp. 1131–1142, Jul. 2005.
 J. Park and I. W. Sandberg, “Approximation and radialbasisfunction networks,” Neural Comput., vol. 5, no. 2, pp. 305–316, 1993.
 W. A. Light, “Some aspects of radial basis function approximation,” Approximation Theory, Spline Functions Appl., vol. 356, no. 2, pp. 163–190, 1992.
 M. Truk and A. Pentland, J. Cognit. Neurosci., “Eigenfaces for Recognition,” vol. 3, no. 1, pp. 71–86, 1991.
 M. D. Richard and R. P. Lippmann, “Neural network classifiers estimate Bayesian a posteriori probabilities,” Neural Comput., vol. 3, no. 4, pp. 461–483, 1991.
 J. Park and I. W. Sandberg, “Universal approximation using radialbasis function networks,” Neural Comput., vol. 3, no. 2, pp. 246–257, 1991.
 K. Hornik, M. Stinchcombe, and H. White, “Multilayer feed forward networks are universal approximators,” Neural Netw., vol. 2, no. 5, pp. 359–366, 1989.
