Browse > Article
http://dx.doi.org/10.3745/KTSDE.2013.2.7.503

Preprocessing and Facial Feature Robust to Illumination Variations  

Kim, Dong-Ju (대구경북과학기술원 IT융합연구부)
Lee, Sang-Heon (대구경북과학기술원 IT융합연구부)
Kim, Hyun-Duk (대구경북과학기술원 IT융합연구부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.7, 2013 , pp. 503-506 More about this Journal
Abstract
In this paper, we propose the face recognition method combining the ECSP preprocessing technique which is modified version of previous CS-LBP and the illumination-robust D2D-PCA feature. The performance evaluation of proposed method was carried out using various binary pattern operators and feature extraction algorithms such as well-known PCA and 2D-PCA on the Yale B database. As a results, the proposed method showed the best recognition accuracy compared to different approaches, and we confirmed that the proposed approach is robust to illumination variation.
Keywords
Face Recognition; Preprocessing; Illumination Variation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 T. Ahonen, A. Hadid, and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.28, No.12, pp.2037-2041, 2006.   DOI   ScienceOn
2 X. Fu, and W. Wei, "Centralized Binary Patterns Embedded with Image Euclidean Distance for Facial Expression Recognition," International Conference of Neural Computation, pp.115-119, 2008.
3 H. Marko, P. Matti, and S. Cordelia, "Description of Interest Regions with Center-Symmetric Local Binary Patterns," Indian Conference on Computer Vision, Graphics and Image Processing, pp.58-69, 2006.
4 Sang-Heon Lee, Dong-Ju Kim and Jin-Ho Cho, "Illumination-robust face recognition system based on differential components", IEEE Transactions on Consumer Electronics, Vol.58, No.3, pp.963-970, 2012.   DOI   ScienceOn
5 Y. Jian, Z. David, F. Alejandro, and J.Y. Yang, "Twodimensional PCA: A New Approach to Appearance-based Face Representation and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.1, pp.131-137, 2004.   DOI   ScienceOn
6 M. Turk and A. Pentland, "Eigenfaces for recognition", Journal of Cognitive Neurosci, Vol.3, No.1, pp.71-86, 1991.   DOI   ScienceOn
7 A. Georghiades, P. Belhumeur, and D. Kriegman, "From few to many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.23, No.6, pp.643-660, 2001.   DOI   ScienceOn