A Novel Algorithm for Face Recognition From Very Low Resolution Images

Title & Authors
A Novel Algorithm for Face Recognition From Very Low Resolution Images
Senthilsingh, C.; Manikandan, M.;

Abstract
Face Recognition assumes much significance in the context of security based application. Normally, high resolution images offer more details about the image and recognizing a face from a reasonably high resolution image would be easier when compared to recognizing images from very low resolution images. This paper addresses the problem of recognizing faces from a very low resolution image whose size is as low as $\small{8{\times}8}$. With the use of CCTV(Closed Circuit Television) and with other surveillance camera-based application for security purposes, the need to overcome the shortcomings with very low resolution images has been on the rise. The present day face recognition algorithms could not provide adequate performance when employed to recognize images from VLR images. Existing methods use super-resolution (SR) methods and Relation Based Super Resolution methods to construct from very low resolution images. This paper uses a learning based super resolution method to extract and construct images from very low resolution images. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.
Keywords
Face recognition;Face super-resolution (SR);Relationship learning;Very low resolution(VLR);
Language
English
Cited by
References
1.
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.

2.
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.

3.
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.

4.
W. Freeman, E. Pasztor, and O. Carmichael, “Learning low-level vision,” Int. J. Comput. Vis., vol. 40, no. 1, pp. 25-47, Oct. 2000.

5.
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.

6.
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.

7.
D. Glasner, S. Bagon, and M. Irani, “Super-resolution from a single image,” in Proc. IEEE Int. Conf. Comput. Vis., 2009, pp. 349-356.

8.
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.

9.
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.

10.
K. Jia and S. Gong, “Generalized face super-resolution,” IEEE Trans. Image Process., vol. 17, no. 6, pp. 873-886, Jun. 2008.

11.
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.

12.
B. Li, H. Chang, S. Shan, X. Chen, and W. Gao, “Hallucinating facial images and features,” in Proc. IEEE Int. Conf. Pattern Recognit., 2008,

13.
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.

14.
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.

15.
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.

16.
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.

17.
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.

18.
X. Ma, J. Zhang, and C. Qi, “Hallucinating face by position-patch,” Pattern Recognit., vol. 43, no. 6, pp. 2224-2236, Jun. 2010.

19.
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.

20.
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.