Browse > Article
http://dx.doi.org/10.9728/dcs.2015.16.5.671

An Improved Joint Bayesian Method using Mirror Image's Features  

Han, Sunghyu (School of Liberal Arts, Korea University of Technology and Education)
Ahn, Jung-Ho (Division of Computer Media Information Engineering, Kangnam University)
Publication Information
Journal of Digital Contents Society / v.16, no.5, 2015 , pp. 671-680 More about this Journal
Abstract
The Joint Bayesian[1] method was published in 2012. Since then, it has been used for binary classification in almost all state-of-the-art face recognition methods. However, no improved methods have been published so far except 2D-JB[2]. In this paper we propose an improved version of the JB method that considers the features of both the given face image and its mirror image. In pattern classification, it is very likely to make a mistake when the value of the decision function is close to the decision boundary or the threshold. By making the value of the decision function far from the decision boundary, the proposed method reduces the errors. The experimental results show that the proposed method outperforms the JB and 2D-JB methods by more than 1% in the challenging LFW DB. Many state-of-the-art methods required tons of training data to improve 1% in the LFW DB, but the proposed method can make it in an easy way.
Keywords
Face recognition; Joint Bayesian method; 2D-JB method; mirror image; LFW DB;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 D. Chen, X. Cao, L. Wang, F. Wen, and J. Sun, "Bayesian Face Revisited: A Joint Formulation," Proc. ECCV, pp.566-579, 2012.
2 S. Han, I.-Y. Lee, J.-H. Ahn, "Two-dimensional Joint Bayesian method for face verification", Journal of Information Processing Systems, in press, 2015.
3 G. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, "Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments", University of Massachusetts, Amherst, Technical Report 07-49, Oct. 2007.
4 X. Cao, D. Wipf, F. Wen, and G. Duan, "A practical Tranfer Learning Algorithm for Face Verification", Proc. ICCV, pp.3208-3215, Dec. 2013.
5 D. Chen, X. Cao, F. Wen, and J. Sun, "Blessing of Dimensionality: High-dimensional Feature and Its Efficient Compression for Face Verification", Proc. CVPR, pp.3025-3032, June. 2013.
6 Y. Sun, Y. Chen, X. Wang and X. Tang, "Deep Learning Face Representation by Joint Identity-Verification", Proc. NIPS, Dec. 2014.
7 Y. Sun, X. Wang, and X. Tang, "Deep Learning Face Representation from Predicting 10,000 Classes", Proc. CVPR, pp.1891-1898, June, 2014.
8 Y. Sun, X. Wang, and X. Tang, "Deeply learned face representations are sparse, selective, and robust", ArXiv:1412.1265, Dec. 2014.
9 L. Wolf, T. Hassner, and Y. Taigman, "Effective Face Recognition by Combining Multiple Descriptors and Learned Background Statistics", IEEE TPAMI, Vol.33, No.10, 2011.
10 C.M. Bishop, Pattern Recognition and Machine Learning, 1st ed., Springer, 2006.
11 C. Cortes and V. Vapnik, "Support-Vector Networks", Machine Learning, Vol.20, No.3, pp.273-297, 1995   DOI
12 T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: Application to face recognition", IEEE TPAMI, Vol.28, No.12, pp.2037-2041, 2006.   DOI
13 X. Xiong and F.D. Torre, "Supervised Descent Method and its Application to Face Alignment", Proc. CVPR, pp.532-539, June, 2013.
14 J.-H. Ahn, "An Improved RSR Method to Obtain the Sparse Projection Matrix", Journal of Digital Contents Society, Vol.16, No.4, pp.605-613, 2015.   DOI