Articles

Symmetrical Face Images and Gamma Correction Technique for Face Recognition under Varying Illumination

Main Article Content

Eva Y Puspaningrum

Abstract

Face recognition with various illumination is one of the most important challenges for practical face recognition systems. Under uncontrolled lighting condition is more significant than the physical characteristics of the individual face. The Impact of lighting uncontrolled lighting from the right and left side so that it can affect the face image. There are many studies on face recognition, but only little attention is paid to   face image is an axis-symmetrical objects. Few studies to explore and exploit the axis-symmetrical property of faces for face recognition are conducted. In this paper, we discuss the pre-processing method to solve one of the common problems in face images with lighting variations. We take the axis-symmetrical of faces then a gamma-correction process for pre-processing that will then be done by robust regression classification (RRC). Experimental results on Yale Face Database B 50x50 with illumination problems show that the proposed technique improves recognition accuracy especially in images with extreme lighting conditions using gamma correction parameter γ = 0.3

Downloads

Download data is not yet available.

Article Details

How to Cite
PUSPANINGRUM, Eva Y. Symmetrical Face Images and Gamma Correction Technique for Face Recognition under Varying Illumination. Jurnal Ilmiah Kursor, [S.l.], v. 9, n. 2, apr. 2018. ISSN 2301-6914. Available at: <http://kursorjournal.org/index.php/kursor/article/view/142>. Date accessed: 22 apr. 2018.
Section
Articles

References

[1] A. K. Singh and G. C. Nandi, “Face recognition using facial symmetry,” Proc. Second Int. Conf. Comput. Sci. Eng. Inf. Technol. - CCSEIT ’12, no. February, pp. 550–554, 2012.

[2] C. C. Chude-Olisah, G. Sulong, U. A. K. Chude-Okonkwo, and S. Z. M. Hashim, “Illumination normalization for edge-based face recognition using the fusion of RGB normalization and gamma correction,” IEEE ICSIPA 2013 - IEEE Int. Conf. Signal Image Process. Appl., no. July 2014, pp. 412–416, 2013.

[3] C. Engineering, “Gamma Correction Technique Based Feature Extraction for Face Recognition System,” vol. 3, no. 1, pp. 20–26, 2013.

[4] H. W. H. Wang, S. Z. Li, and Y. W. Y. Wang, “Face recognition under varying lighting conditions using self quotient image,” Sixth IEEE Int. Conf. Autom. Face Gesture Recognition, 2004. Proceedings., pp. 2–7, 2004.

[5] I. Naseem, R. Togneri, and M. Bennamoun, “Robust regression for face recognition,” Pattern Recognit., vol. 45, no. 1, pp. 104–118, 2012.

[6] M.-C. Su and C.-H. Chou, “Application of associative memory in human face detection,” Neural Networks, 1999. IJCNN ’99. Int. Jt. Conf., vol. 5, pp. 3194–3197 vol.5, 1999.

[7] R. Basri and D. Jacobs, “Lambertian reflectances and linear subspaces,” IEEE Int. Conf. Comput. Vis., vol. 0, no. C, pp. 383–390, 2001.

[8] R. Ramamoorthi and P. Hanrahan, “On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object,” J. Opt. Soc. Am. A, vol. 18, no. 10, p. 2448, 2001.

[9] S. Anila and N. Devarajan, “Preprocessing Technique for Face Recognition,” Glob. J. Comput. Sci. Technol. Graph. Vis., vol. 12, no. 11, pp. 13–18, 2012.

[10] Shiguang Shan, Wen Gao, Bo Cao, and Debin Zhao, “Illumination normalization for robust face recognition against varying lighting conditions,” 2003 IEEE Int. SOI Conf. Proc. (Cat. No.03CH37443), pp. 157–164, 2010.

[11] S. M. Pizer et al., “Adaptive Histogram Equalization and Its Variations.,” Comput. vision, Graph. image Process., vol. 39, no. 3, pp. 355–368, 1987.

[12] W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain.,” IEEE Trans. Syst. Man. Cybern. B. Cybern., vol. 36, no. 2, pp. 458–466, 2006.

[13] Y. Adini, Y. Moses, and S. Ullman, “Face recognition: The problem of compensating for changes in illumination direction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 721–732, 1997.

[14] Y. Xu, Z. Zhang, G. Lu, and J. Yang, “Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification,” Pattern Recognit., vol. 54, pp. 68–82, 2016.

[15] Z. Liu, J. Pu, Q. Wu, and X. Zhao, “Using the original and symmetrical face training samples to perform collaborative representation for face recognition,” Optik (Stuttg)., vol. 127, no. 4, pp. 1900–1904, 2016.