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Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern

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Abstract

To investigate the robustness of face recognition algorithms under the complicated variations of illumination, facial expression and posture, the advantages and disadvantages of seven typical algorithms on extracting global and local features are studied through the experiments respectively on the Olivetti Research Laboratory database and the other three databases (the three subsets of illumination, expression and posture that are constructed by selecting images from several existing face databases). By taking the above experimental results into consideration, two schemes of face recognition which are based on the decision fusion of the two-dimensional linear discriminant analysis (2DLDA) and local binary pattern (LBP) are proposed in this paper to heighten the recognition rates. In addition, partitioning a face non-uniformly for its LBP histograms is conducted to improve the performance. Our experimental results have shown the complementarities of the two kinds of features, the 2DLDA and LBP, and have verified the effectiveness of the proposed fusion algorithms.

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References

  1. Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71–86

    Article  Google Scholar 

  2. Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711–720

    Google Scholar 

  3. Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037–2041

    Article  MATH  Google Scholar 

  4. Shen L L, Bai L. A review on Gabor wavelets for face recognition. Pattern Analysis and Applications, 2006, 9(2): 273–292

    Article  MathSciNet  Google Scholar 

  5. Makwana R M. Illumination invariant face recognition: a survey of passive methods. Procedia Computer Science, 2010, 2(1): 101–110

    Article  Google Scholar 

  6. Murphy-Chutorian E, Trivedi M M. Head pose estimation in computer vision: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(4): 607–626

    Article  Google Scholar 

  7. Wang H, Li S Z, Wang Y. Face recognition under varying lighting conditions using self quotient image. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. 2004, 819–824

    Google Scholar 

  8. Zhang T, Tang Y Y, Fang B, Shang Z, Liu X. Face recognition under varying illumination using gradientfaces. IEEE Transactions on Image Processing, 2009, 18(11): 2599–2606

    Article  MathSciNet  Google Scholar 

  9. Zhang D, Zhou Z H. (2D)2PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing, 2005, 69(1): 224–231

    Article  Google Scholar 

  10. Noushath S, Kumar G H, Shivakumara P. (2D)2LDA: an efficient approach for face recognition. Pattern Recognition, 2006, 39(7): 1396–1400

    Article  MATH  Google Scholar 

  11. Nagesh P, Li B X. A compressive sensing approach for expressioninvariant face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1518–1525

    Google Scholar 

  12. Sharma A, Al Haj M, Choi J, Davis L S, Jacobs D W. Robust pose invariant face recognition using coupled latent space discriminant analysis. Computer Vision and Image Understanding, 2012, 116(11): 1195–1110

    Article  Google Scholar 

  13. Lei Y Q, Lai H B, Li Q M. Geometric features of 3D face and recognition of it by PCA. Journal of Multimedia, 2011, 6(2): 207–216

    Article  Google Scholar 

  14. Lei Y Q, Lai H B, Jiang X T. 3D face recognition by surf operator based on depth image. In: Proceedings of the 3rd IEEE International Conference on Computer Science and Information Technology. 2010, 240–244

    Google Scholar 

  15. Lei Y Q, Chen D J, Yuan M L, Li Q M, Shi Z X. 3D face recognition by surface classification image and PCA. In: Proceedings of the 2nd International Conference on Machine Vision. 2009, 145–149

    Google Scholar 

  16. Chowdhury S, Sing J K, Basu D K, Nasipuri M. Face recognition by fusing local and global discriminant features. In: Proceedings of the 2nd International Conference on Emerging Applications of Information Technology. 2011, 102–105

    Google Scholar 

  17. Kim C, Oh J O, Choi C H. Combined subspace method using global and local features for face recognition. In: Proceedings of the IEEE International Joint Conference on Neural Networks. 2005, 2030–2035

    Google Scholar 

  18. Lin D H, Tang X O. Recognize high resolution faces: from macrocosm to microcosm. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, 1355–1362

    Google Scholar 

  19. Su Y, Shan S G, Chen X L, Gao W. Integration of global and local feature for face recognition. Journal of Software, 2010, 21(8): 1849–1862

    Article  MATH  Google Scholar 

  20. Geng C, Jiang X. Fully automatic face recognition framework based on local and global features. Machine Vision and Applications, 2012, 24(3): 537–549

    Article  Google Scholar 

  21. Bai X, Rao C, Wang X G. Shape vocabulary: a robust and efficient shape representation for shape matching. IEEE Transactions on Image Processing, 2014, 23(9): 3935–3949

    Article  MathSciNet  Google Scholar 

  22. Zhang M L, Wu L. Lift: multi-label learning with label-specific features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 107–120

    Article  Google Scholar 

  23. Zhang D Q, Wang Y P, Zhou L P, Yuan H, Shen D G. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage, 2011, 55(3): 856–867

    Article  Google Scholar 

  24. Pong K H, Lam K M. Multi-resolution feature fusion for face recognition. Pattern Recognition, 2014, 47(2): 556–567

    Article  Google Scholar 

  25. Cament L A, Castillo L E, Perez J P, Galdames F J, Perez C A. Fusion of local normalization and Gabor entropy weighted features for face identification. Pattern Recognition, 2014, 47(2): 568–577

    Article  Google Scholar 

  26. Tang J H, Li Z C, Wang M, Zhao R Z. Neighborhood discriminant hashing for large-scale image retrieval. IEEE Transactions on Image Processing, 2015, 24(9): 2827–2840

    Article  MathSciNet  Google Scholar 

  27. Li Z C, Liu J, Tang J H, Lu H Q. Robust structured subspace learning for data representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(10): 2085–2098

    Article  Google Scholar 

  28. Li Z C, Liu J, Yang Y, Zhou X F, Lu H Q. Clustering-guided sparse structural learning for unsupervised feature selection. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(9): 2138–2150

    Article  Google Scholar 

  29. Wang D H, Wang X K, Kong S. Integration of multi-feature fusion and dictionary learning for face recognition. Image and Vision Computing, 2013, 31(12): 895–904

    Article  Google Scholar 

  30. Chan C H, Tahir M A, Kittler J, Matti P, Pietikainen M. Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(5): 1164–1177

    Article  Google Scholar 

  31. Wang WH, Yang J, Xiao J W, Li S, Zhou D X. Face recognition based on deep learning. Lecture Notes in Computer Science, 2015, 8944: 812–820

    Article  Google Scholar 

  32. Sun Y, Wang X G, Tang X O. Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014, 1891–1898

    Google Scholar 

  33. Liu M Y, Li S X, Shan S G, Chen X L. AU-inspired deep networks for facial expression feature learning. Neurocomputing, 2015, 159(1): 126–136

    Article  Google Scholar 

  34. Wang B B, Hao X J, Chen L S, Cui J M, Lei Y Q. Face recognition based on the feature fusion of 2DLDA and LBP. In: Proceedings of the 4th International Conference on Information, Intelligence, Systems and Applications. 2013, 155–158

    Google Scholar 

  35. Ye J P, Janardan R, Li Q. Two-dimensional linear discriminant analysis. Advances in Neural Information Processing Systems, 2004, 17: 1569–1576

    Google Scholar 

  36. Wang X M, Huang C, Fang X Y, Liu J G. 2DPCA vs. 2DLDA: face recognition using two-dimensional method. In: Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence. 2009, 357–360

    Google Scholar 

  37. Yu W X, Wang Z Z, Chen W T. A new framework to combine vertical and horizontal information for face recognition. Neurocomputing, 2009, 72(4): 1084–1091

    Article  Google Scholar 

  38. Samaria F S, Harter A C. Parameterisation of a stochastic model for human face identification. In: Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision. 1994, 138–142

    Google Scholar 

  39. Lee K C, Ho J, Kriegman D J. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 684–698

    Article  Google Scholar 

  40. Xu C H, Tan T N, Wang Y H, Quan L. Combining local features for robust nose location in 3D facial data. Pattern Recognition Letters, 2006, 27(13): 1487–1494

    Article  Google Scholar 

  41. Sim T, Baker S, Bsat M. The CMU pose, illumination, and expression (PIE) database. In: Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition. 2002, 46–51

    Google Scholar 

  42. Stathopoulou I O, Tsihrintzis G A. Appearance-based face detection with artificial neural networks. Intelligent Decision Technologies, 2011, 5(2): 101–111

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, School of Information Science and Engineering, Xiamen University, Xiamen, 361005, China

    Qicong Wang, Binbin Wang, Xinjie Hao, Lisheng Chen, Jingmin Cui, Rongrong Ji & Yunqi Lei

Authors
  1. Qicong Wang
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  2. Binbin Wang
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  3. Xinjie Hao
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  4. Lisheng Chen
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  5. Jingmin Cui
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  6. Rongrong Ji
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  7. Yunqi Lei
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Corresponding author

Correspondence to Yunqi Lei.

Additional information

Qicong Wang received the PhD degree from Zhejiang University, China in 2007. He is currently an associate professor at the Department of Computer Science, Xiamen University, China. His research interests include pattern recognition, machine learning and computer vision.

Binbin Wang received the BE degree and the ME degree in 2010 and 2013, respectively, both from Xiamen University, China. She is currently a software engineer at the center of software development of Industrial and Commercial Bank of China, Guangdong.

Xinjie Hao received the BE degree and the ME degree in 2010 and 2013, respectively, both from Xiamen University, China. He is currently a postgraduate student majoring computer science at Worcester Polytechnic Institute, USA. His research interests include machine learning and image processing.

Lisheng Chen received the BE degree and the ME degree in 2010 and 2013, respectively, both from Xiamen University, China. He is currently a technical manager at the department of Internet products of Hebao Financial Information Consultant Co. Ltd., Shenzhen, China.

Jingmin Cui received the BE degree and the ME degree in 2010 and 2013, respectively, both from Xiamen University, China. She is currently a system analyst (SA) at the Research and Development Center (Shenzhen), the Bank of Communications, China.

Rongrong Ji received the PhD degree from Harbin Institute of Technology, China. He had been a Postdoctoral Research Fellow in Columbia University, USA. Currently he is a Minjiang Chair Professor of Xiamen University, China. His research interests include machine learning and computer vision.

Yunqi Lei received the BE degree from the University of Science and Technology of China, China in 1982, and the PhD degree from the National University of Defense Technology, China in 1988. Currently, he is a professor at the Department of Computer Science, Xiamen University, China. His research interests include machine learning and computer vision.

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Wang, Q., Wang, B., Hao, X. et al. Face recognition by decision fusion of two-dimensional linear discriminant analysis and local binary pattern. Front. Comput. Sci. 10, 1118–1129 (2016). https://doi.org/10.1007/s11704-016-5024-6

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  • DOI: https://doi.org/10.1007/s11704-016-5024-6

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