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An Improved Joint Bayesian Method using Mirror Image's Features
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 Title & Authors
An Improved Joint Bayesian Method using Mirror Image's Features
Han, Sunghyu; Ahn, Jung-Ho;
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 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;
 Language
Korean
 Cited by
1.
희소 투영행렬 획득을 위한 RSR 개선 방법론,안정호;

디지털콘텐츠학회 논문지, 2015. vol.16. 4, pp.605-613 crossref(new window)
 References
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

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. crossref(new window)

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. crossref(new window)