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http://dx.doi.org/10.5573/ieie.2016.53.2.097

Face Recognition Using Histograms of Multi-resolution Segments Based on Discriminant Face Descriptor  

Lee, Jang-yoon (Department of Computer Science and Engineering, Dankook University)
Lee, Yonggeol (Department of Computer Science and Engineering, Dankook University)
Choi, Sang-Il (Department of Computer Science and Engineering, Dankook University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.53, no.2, 2016 , pp. 97-105 More about this Journal
Abstract
We propose a face recognition method using the histograms of multi-resolution segments in order to effectively utilize the local information of faces. Since the variations in faces can occur in various sizes, the DFD method, which uses the histograms from the sub-regions of the same size, is not effective for obtaining local information of faces. In this paper, we first divide an image into several sub-regions and extract the DFD(Discriminant Face Descriptor) from each sub-region. By dividing each sub-region into several segments with multi-resolution and extracting histograms for each segment, we reduce the loss of local information in the process of recognition. The experimental results for the Yale B, AR, CAS-PEAL-R1 databases show that the proposed method improves the recognition performance compared to the existing DFD based method.
Keywords
Face Recognition; Discriminant Face Descriptor; Multi-resolution Segmentation; Illumination Variation;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 D. Lowe, "Distinctive Image Features From Scale-invariant Keypoints," International Journal of Computer Vision, Vol. 60, no. 2, pp. 91-110, 2004.   DOI
2 K. Mikolajczyk and C. Schmid, "A Performance Evaluation of Local Descriptors," IEEE Trans., Pattern Analysis and Machine Intelligence, Vol. 27, no. 10, pp. 1615-1630, 2005.   DOI
3 H. Bay, A. Ess, T. Tuytelaars and L.V. Gool, " Speeded Up Robust Features (SURF)," Computer Vision and Image Understanding, Vol. 110, no. 3, pp. 346-359, 2008.   DOI
4 Z. Ramin and J. Woodfill. "Non-parametric Local Transforms for Computing Visual Correspondence," Lecture Notes in Computer Science (European Conf. Computer Vision), Vol. 801, pp. 618-629, 1994.
5 B. Froba and A. Ernstm, "Face Detection with the Modified Census Transform," in Proc. IEEE Conf. Automatic Face and Gesture Recognition, pp. 91-96, May 2004.
6 T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 28, no. 12, pp. 2037-2041, 2006.   DOI
7 Z. Cao, Q. Yin, X. Tang and J. Sun, "Face Recognition with Learning-Based Descriptor," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2707-2714, June 2010.
8 D. Maturana, D. Mery and A. Soto, "Face Recognition with Decision Tree-Based Local Binary Patterns," Lecture Notes in Computer Science (Asian Conf. Computer Vision), Vol. 6495, pp. 618-629, 2010.
9 D.J. Kim, S.H. Lee and M.K. Sohn, "A Study on Face Recognition Method Based on Binary Pattern Image Under Varying Lighting Condition," Journal of the Institute of Electronics and Information Engineers, Vol. 49, no. 2, pp. 61-74, 2012.
10 S.J. Lee, D.H. Kim, Suryanto and S.J. Ko, " Improved Color-LBP Joint Histogram for Robust Object Tracking," Journal of the Institute of Electronics and Information Engineers, Vol. 47, no. 11, pp. 604-607, 2011.
11 T. Ojala, M. Pietikanen and T. Manpaa, " Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, no. 7, pp. 971-987, 2002.   DOI
12 M. Heikkila, M. Pietikainen and C. Schmid, " Description of Interest Regions with Local Binary Patterns," Pattern Recognition, Vol. 42, no. 3, pp. 425-436, 2009.   DOI
13 M. Calonder, V. Lepetit, C. Strecha and P. Fua, "BRIEF: Binary Robust Independent Elementary Features," Lecture Notes in Computer Science (European Conf. Computer Vision), Vol. 6314, pp. 778-792, 2010.
14 Y. Rubner, C. Tomasi and L.J. Guibas. "The Earth Mover's Distance as a Metric for Image Retrieval," International Journal of Computer Vision, Vol. 40, no. 2, pp. 99-121, 2000.   DOI
15 M. Daniel, Domingo Mery, and Alvaro Soto, " Learning Discriminative Local Binary Patterns for Face Recognition," in Proc. IEEE Conf. Automatic Face and Gesture Recognition and Workshops, pp. 470-475, March 2011.
16 P.N. Belhumeur, J.P. Hespanha and D.J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 19, no. 7, pp. 711-720, 1997.   DOI
17 L. Zhen, M. Pietikainen and S. Li, "Learning Discriminant Face Descriptor," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 36, no. 2, pp. 289-302, 2014.   DOI
18 A.S. Georghiades, S. Athinodoros, P.N. Belhumeur and J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, no. 6, pp. 643-660, 2001.   DOI
19 A.M. Marttinez and R. Benavente, The AR Face Database, CVC Technical Report #24, June 1998.
20 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 Evaluation," IEEE Trans. Systems, Man, and Cybernetics, Part A : Systems and Humans, Vol. 38, no. 1, pp. 149-161, January 2008.   DOI
21 Z. Lei and S.Z. Li, "Learning Discriminant Face Descriptor for Face Recognition," in Proc. Asian Conf. Computer Vision, pp. 748-759, November 2012.
22 L. Zhen, L. Shengcai, K. Jain and S.Z. Li, " Coupled Discriminant Analysis for Heterogeneous Face Recognition," IEEE Trans. Information Forensics and Security, Vol. 7, no. 6, pp. 1707-1716, 2012.   DOI
23 X. Tan and B. Triggs, "Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions," IEEE Trans. Image Processing, Vol. 19, no. 6, pp. 1635-1650, 2010.   DOI
24 J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," in Proc. Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281-297, 1967.
25 S.H. Kim, S.T. Chung, S.H. Jung and S.W. Jo, "Robust Eye Localization Using Multi-Scale Gabor Feature Vectors," Journal of the Institute of Electronics and Information Engineers, Vol. 45, no. 1, pp. 25-36, 2008.