S. Baker and T. Kanade, “Limits onn super-resoluution and how to break them,” IEEE Traans. Pattern AAnal. Mach. Inttell., vol. 24, no. 9, pp.1677-1183, Sep. 2002.
A. Chakrabarti, A. N. Rajagopalan, and R. Chellappa, “Super-resolution of face images uusing kernel PPCA-based prior,” IEEE Trans. Multimedia, vol. 9, no. 4, pp. 888-892, Jun. 2007.
G. Cristóbal, E. Gil, F.Šroubek, J. Flusser, C. Miravet, and F. Rodrıa-cute; guez, uperresolution imaging: A survey of current techniques,” in Proc. Adv. Signal Process. Algorithms, rchitectures, Implementations XVIII, 2008, vol. 7074, pp. 0C1-0C18.
W. Freeman, E. Pasztor, and O. Carmichael, “Learning low-level vision,” Int. J. Comput. Vis., vol. 40, no. 1, pp. 25-47, Oct. 2000.
W. Gao, B. Cao, S. Shan, X. Chen, D. Zhou, X. Zhang, and D. Zhao, “The CAS-PEAL large- scale Chinese face database and baseline evaluations,” IEEE Trans. Syst., Man, Cybern A, Syst., Humans, vol. 38, no. 1, pp. 149-161, Jan. 2008.
A. Georghiades, P. Belhumeur, annd D. Kriegman, “From few to many: Illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6 , pp. 643-660, Jun. 2001.
D. Glasner, S. Bagon, and M. Irani, “Super-resolution from a single image,” in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 349-356.
M. Grgic, K. Delac, and S. Grgic, “SCface-surveillance cameras face database,” Multimedia Tools Appl. J., vol. 51, no. 3,, pp. 863-879,, Feb. 2011.
P. H. Hennings-Yeomans, S. Baker, and B. Kumar, “Simultaneous super-reeolution and feature extraction for recognition of low-resolution faces,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., 2008, pp.1-8.
K. Jia and S. Gong, “Generalized face super-resolution,” IEEE Trans. Image Process., vol. 17, no. 6, pp. 873-886, Jun. 2008.
K. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 6, pp. 1127-1133, Jun. 2010.
B. Li, H. Chang, S. Shan, X. Chen, and W. Gao, “Hallucinating facial images and features,” in Proc. IEEE Int. Conf. Pattern Recognit., 2008,
H. Li, T. Jiang, and K. Zhang, “Efficient and robust feature extraction by maximum margin criterion,” IEEE Trans. Neural Netw., vol. 17, no .1, pp. 157-1665, Jan.2006.
C. Liu, “Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance,” IEEE TransPattern Anal. Mach. Intell., vol. 28, no. 5, pp. 725-737, May 2006.
C. Liu, H. Y. Shum, and W. T. Freeman, “Face hallucination: Theoryand practice,” Int. J. Comput. Vis., vol. 75, no. 1, pp. 115-134, Oct. 2007.
W. Liu, D. Lin, and X. Tang, “Hallucinating faces: Tensorpatch super-resolution and coupled residue compensation,” in Proc. IEEE Int. Conf.Comput. Vision Pattern Recognit.,, 2005, pp. 478-484.
Y. M. Lui, D. Bolme, B. A. Draper, J. R. Beveridge, G. Givens, and P.J. Phillips, “A meta-analysis of face recognition covariates,” in Proc.Int. Conf. Biometrics: Theory, Appl. Syst., 2009, pp. 1-8.
X. Ma, J. Zhang, and C. Qi, “Hallucinating face by position-patch,” Pattern Recognit., vol. 43, no. 6, pp. 2224-2236, Jun. 2010.
J. S. Park and S. W. Lee, “An example-based face hallucination method for single-frame , low-resolution facial images,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1806-1816, Oct. 2008.
W. W. Zou and P. C. Yuen, “Face superresolution,” in Emerging Topics in Computer Vision and its Applications, C. Chen, Ed. Singapore: World Scientific, 2011, ch. 2.