KSII Transactions on Internet and Information Systems (TIIS)

  • 제11권11호
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  • Pages.5474-5490
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  • 2017
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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RowAMD Distance: A Novel 2DPCA-Based Distance Computation with Texture-Based Technique for Face Recognition

  • Al-Arashi, Waled Hussein (Electronics Engineering Department, University of Science and Technology) ;
  • Shing, Chai Wuh (Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia) ;
  • Suandi, Shahrel Azmin (Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia)
  • 투고 : 2017.01.31
  • 심사 : 2017.07.24
  • 발행 : 2017.11.30
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초록

Although two-dimensional principal component analysis (2DPCA) has been shown to be successful in face recognition system, it is still very sensitive to illumination variations. To reduce the effect of these variations, texture-based techniques are used due to their robustness to these variations. In this paper, we explore several texture-based techniques and determine the most appropriate one to be used with 2DPCA-based techniques for face recognition. We also propose a new distance metric computation in 2DPCA called Row Assembled Matrix Distance (RowAMD). Experiments on Yale Face Database, Extended Yale Face Database B, AR Database and LFW Database reveal that the proposed RowAMD distance computation method outperforms other conventional distance metrics when Local Line Binary Pattern (LLBP) and Multi-scale Block Local Binary Pattern (MB-LBP) are used for face authentication and face identification, respectively. In addition to this, the results also demonstrate the robustness of the proposed RowAMD with several texture-based techniques.

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참고문헌

  1. Heitmeyer, R. "Biometric Identification Promises Fast and Secure processing of Airline Passengers," The International Civil Aviation Organization Journal, 55(9): 10-11, 2000.
  2. MRTD, Machine-readable travel documents (MRTD), 2000. Available from: .
  3. Sirovich, L., & Kirby, M., "Low-dimensional procedure for the characterization of human faces.," Josa a, 4(3): 519-524, 1987. https://doi.org/10.1364/JOSAA.4.000519
  4. Moon, H. and Phillips, P. J., "Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms," Perception-London, 30(3): 303-322, 2001. https://doi.org/10.1068/p2896
  5. Tjahyadi, R., Liu, W. and Venkatesh, S., "Automatic Parameter Selection for Eigenfaces," Pacific Journal of Optimization, 2(2): 277-288, 2006.
  6. Meng, H. and Ke, X., "Further Research on Principal Component Analysis Method of Face Recognition," in Proc. of IEEE International Conference on Mechatronics and Automation, pp. 421-425. 2008.
  7. Barnouti, N. H., Al-Dabbagh, S. S. M., and Al-Bamarni, M. H. J., "Real-Time Face Detection and Recognition using Principal Component Analysis (PCA)- back Propagation Neural Network (BPNN) and Radial Basis Function (RBF)," Journal of Theoretical and Applied Information Technology, 91(1): 28-34, 2016.
  8. Turk, M. A., & Pentland, A. P., "Face recognition using eigenfaces," in Proc. of IEEE Proceedings CVPR'91., IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
  9. Zhao, W. and Chellappa, R., Face Processing: Advanced Modeling and Methods, Inc. Academic Press, 2006.
  10. Wiskott, L., Fellous, J.M., Kuiger, N. and Von der Malsburg, C. "Face Recognition by Elastic Bunch Graph Matching," Computer Analysis of Images and Patterns, Springer, pp. 456-463, 1997.
  11. Yang, J., Zhang, D., Frangi, A. F. and Yang, J. Y., "Two-dimensional PCA: A New Approach To Appearance-Based Face Representation and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1): 131-137, 2004. https://doi.org/10.1109/TPAMI.2004.1261097
  12. Zhang, D., Zhou, Z. H. and Chen, S., "Diagonal Principal Component Analysis for Face Recognition, Pattern Recognition, 39(1): 140-142, 2006. https://doi.org/10.1016/j.patcog.2005.08.002
  13. Koerich, A., Oliveira S, L. and Britto Jr, A., "Face Recognition Using Selected 2DPCA Coefficients," in Proc. of 1IWSSIP 17th International Conference on Systems, Signals and Image Processing, pp. 490-494, 2010.
  14. Zeng, Y., Feng, D. and Xiong, L., "An Algorithm of Face Recognition Based on The Variation of 2DPCA," Journal of Computational Information Systems 7(1): 303-310, 2010.
  15. Draper, B. A., Yambor, W. S. and Beveridge, J. R., "Analyzing PCA-Based Face Recognition Algorithms: Eigenvector Selection and Distance Measures," Empirical Evaluation Methods in Computer Vision, 2002.
  16. Meyer, C., Matrix Analysis and Applied Linear Algebra, Vol. 2, Society for Industrial and Applied Mathematics, 2000.
  17. Liwei, W., Yan, Z. and Jufu, F., "On The Euclidean Distance of Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8): 1334-1339, 2005. https://doi.org/10.1109/TPAMI.2005.165
  18. Zuo, W., Zhang, D. and Wang, K., "An Assembled Matrix Distance Metric for 2DPCA-Based Image Recognition," Pattern Recognition Letters, 27(3): 210-216, 2006. https://doi.org/10.1016/j.patrec.2005.08.017
  19. Meng, J. and Zhang, W., "Volume Measure in 2DPCA-Based Face Recognition," Pattern Recognition Letters, 28(10): 1203-1208, 2007. https://doi.org/10.1016/j.patrec.2007.01.015
  20. Xu, Z., Zhang, J. and Dai, X., "Boosting for learning a similarity measure in 2DPCA based face recognition," World Congress on Computer Science and Information Engineering, Vol. 7, IEEE, pp. 130-134, 2009.
  21. Al-Arashi, W. and Suandi, S., "Row-KNN Distance Computation in 2DPCA Based for Face Recognition," International Journal of Academic Research Part A, 5(1): 139-145, 2013.
  22. Heusch, G. Cardinaux, F. and Marcel, S., "Lighting Normalization Algorithms for Face Verification," Instituto de Investigacion Agropecuaria de Panama Communications (IDIAP-com) 3, 2005.
  23. Phillips, P.J. Moon, H., Rauss, P. and Rizvi S. A., "The Feret Evaluation Methodology for Face-Recognition Algorithms," Image and Vision Computing 16(5): 295-306, 1998. https://doi.org/10.1016/S0262-8856(97)00070-X
  24. Kim, T. K. and Kittler, J., "Locally Linear Discriminant Analysis for Multimodally Distributed Classes for Face Recognition with A Single Model Image," IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(3): 318-327, 2005. https://doi.org/10.1109/TPAMI.2005.58
  25. Zhao, W., Chellappa, R., Phillips, P. J. and Rosenfeld, A., "Face recognition: A Literature Survey," ACM Computing Surveys 35(4): 399-458, 2003. https://doi.org/10.1145/954339.954342
  26. Moses, Y. Adini, Y. and Ullman, S., "Face Recognition: The Problem of Compensating for Changes in Illumination Direction," in Proc. of the 3rd European Conference on Computer Vision, Lecture Notes in Computer Science, Springer, pp. 286-296, 1994.
  27. Anbang, X., Xin, J., Yugang, J. and Ping, G., "Complete two-dimensional PCA for face recognition," in Proc. of 18th International Conference on Pattern Recognition, 2006. ICPR 2006., Vol. 3, pp.481-484, 2006.
  28. Shao, M. and Wang, Y., "Joint Features for Face Recognition Under Variable Illuminations," in Proc. of Fifth International Conference on Image and Graphics, IEEE Computer Society, pp. 922-927, 2009.
  29. Wang, Y., Zhang, L., Liu, Z., Hua, G.,Wen, Z., Zhang, Z. and Samaras, D., "Face Relighting From A Single Image Under Arbitrary Unknown Lighting Conditions," IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11): 1968-1984, 2009. https://doi.org/10.1109/TPAMI.2008.244
  30. O'Toole, A. J., Phillips, P. J., Jiang, F., Ayyad, J., Penard, N. and Abdi, H. (2007). Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination, IEEE Transactions on Pattern Analysis and Machine Intelligence 29(9): 1642-1646. https://doi.org/10.1109/TPAMI.2007.1107
  31. Tan, X. and Triggs, B., "Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions," IEEE Transactions Image Processing, 19(6): 1635-1650, 2010. https://doi.org/10.1109/TIP.2010.2042645
  32. Mian, A., "Illumination Invariant Recognition and 3D Reconstruction of Faces Using Desktop Optics," Optics Express, 19(8): 7491-7506, 2011. https://doi.org/10.1364/OE.19.007491
  33. Bozorgtaber, B., Azami, H. and Noorian, F., "Illumination Invariant Face Recognition using Fuzzy lda and ffnn," Journal of Signal and Information Processing, 3(1):45-50, 2012. https://doi.org/10.4236/jsip.2012.31007
  34. Ojala, T., Pietikainen, M. and Harwood, D., "A Comparative Study of Texture Measures with Classification Based On Featured Distributions," Pattern Recognition 29(1): 51-59, 1996. https://doi.org/10.1016/0031-3203(95)00067-4
  35. Jin, H., Liu, Q., Lu, H. and Tong, X., "Face Detection using Improved LBP Under Bayesian Framework," International Conference on Image and Graphics, IEEE Computer Society, Los Alamitos, CA, USA, pp. 306-309, 2004.
  36. Heikkila, M., Pietikainen, M. & Schmid, C., "Description of Interest Regions with Local Binary Patterns," Pattern Recognition, 42(3): 425-436, 2009. https://doi.org/10.1016/j.patcog.2008.08.014
  37. Xiaosheng, W. and Junding, S., "An Effective Texture Spectrum Descriptor," in Proc. of IEEE 5th International Conference on Information Assurance and Security (IAS'09), Vol. 2, pp. 361-364, 2009.
  38. Junding, S. Shisong, Z. and Xiaosheng, W., "Image Retrieval Based On An Improved CS-LBP Descriptor," in Proc. of The 2nd IEEE International Conference on Information Management and Engineering (ICIME), pp. 115-117, 2010.
  39. Liao, S. Zhu, X., Lei, Z., Zhang, L., and Li, S. Z., "Learning Multi-scale Block Local Binary Patterns for Face Recognition," in Proc. of 2nd IAPR/IEEE International Conference on Biometrics, Lecture Notes on Computer Science, 4642, pp. 828-837, 2007.
  40. Zhang, B. Gao, Y., Zhao, S. and Liu, J., "Local Derivative Pattern Versus Local Binary Pattern: Face Recognition with High-order Local Pattern Descriptor," IEEE Transactions Image Processing 19(2): 533-544, 2010. https://doi.org/10.1109/TIP.2009.2035882
  41. Jabid, T., Kabir, M. H. and Chae, O., "Local Directional Pattern (LDP) for Face Recognition," in Proc. of Consumer Electronics (ICCE), International Conference on Digest of Technical Papers, pp. 329-330, 2010.
  42. Petpon, A. and Srisuk, S., "Face Recognition with Local Line Binary Pattern," in Proc. of Fifth International Conference on Image and Graphics (ICIG'09), IEEE Computer Society, Washington, DC, USA, pp. 533-539, 2009.
  43. Georghiades, A. S., Belhumeur, P. N. and Kriegman, D. J., "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose," IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6): 643-660, 2001. https://doi.org/10.1109/34.927464
  44. Lee, K. C., Ho, J. and Kriegman, D. J., "Acquiring Linear Subspaces for Face Recognition under Variable Lighting," IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):684-698, 2005. https://doi.org/10.1109/TPAMI.2005.92
  45. Martinez, A. M. and Kak, A. C., "PCA Versus LDA," IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2): 228-233, 2001. https://doi.org/10.1109/34.908974
  46. Huang, G. B., Mattar, M., Berg, T. and Learned-Miller, E., "Labeled Faces in The Wild: A Database for Studying Face Recognition in Unconstrained Environments," Technical report, University of Massachusetts, Amherst pp. 07-49, 2007.
  47. Peng, Y., Ganesh, A., Wright, J., Xu,W. and Ma, Y., "RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images," IEEE Transactions on Pattern Analysis and Machine Intelligence 34(11): 2233-2246, 2012. https://doi.org/10.1109/TPAMI.2011.282