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Mean Laplacian mappings-based difference LDA for face recognition

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Abstract

This paper proposes a difference LDA based on mean Laplacian mappings. For each pixel, we firstly estimate multiple mean Laplacian mappings which include an odd and even and full mean Laplacian mappings, and generate three different images respectively. Then, we obtain a concatenated image by concatenating the odd, even and full images. The usage of the concatenated mean Laplacian mapping results in a more robust dissimilarity measures between images. In order to derive discriminative representation for the concatenated feature vector, we introduce a difference LDA which applies a difference scatter matrix to find the subspace that best discriminates different face classes. The introduction of the difference scatter matrix avoids the singularity of the within-class scatter matrix. Experiments show that the proposed method for facial expression, illumination change and different occlusion has better robustness, and achieves a higher recognition rate. For a single sample per person, the proposed method can also obtain a higher recognition rate.

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References

  1. Balasubramanian M, Schwartz EL, Tenenbaum JB, de Silva V, Langford JC (2002) The isomap algorithm and topological stability. Science 295(5552)7

  2. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  3. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404

    Article  Google Scholar 

  4. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  5. Belkin M, Niyogi P (2003) Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396

    Article  MATH  Google Scholar 

  6. Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In the 20th International Joint Conference on Artificial Intelligence(IJCAI)

  7. Cai D, He X, Han J (2007) Semi-supervised discriminant analysis, in: IEEE 11th International Conference on Computer Vision (ICCV)

  8. Cai D, He X, Han J (2007) Isometric projection. In Proceedings of AAAI Conference on Artificial Intelligence

  9. Deng C, He X, Han J (2011) Speed up kernel discriminant analysis. VLDB J 20(1):21–33

    Article  Google Scholar 

  10. Deng W, Hu J, Guo J, Cai W, Feng D (2010) Robust, accurate and efficient face recognition from a single training image: a uniform pursuit approach. Pattern Recogn 43(5):1748–1762

    Article  MATH  Google Scholar 

  11. Fu Y, Huang T (2005) Locally linear embedded eigenspace analysis, IFP-TR, University of Illinois at Urbana-Champaign, January

  12. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  13. He XF, Cai D, Yan SC and Zhang HJ (2005) Neighborhood preserving embedding. In IEEE Int’l Conf. on Computer Vision (ICCV)

  14. He XF, Yan SC, Hu YX, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  15. Lee K, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Article  Google Scholar 

  16. Li ZK, Ding LX, He JR, Hu QH (2014) Face feature representation based on image decomposition. J Softw 25(9):2102–2118 (in Chinese)

    Google Scholar 

  17. Li Z, Ding L, Wang Y, et al (2014) Face representation with gradient orientations and euler mapping: application to face recognition. Int J Pattern Recognit Artif Intell 28(08)

  18. Lu J, Tan Y-P, Wang G (2013) Discriminative multimanifold analysis for face recognition from a single training sample per person. IEEE Trans Pattern Anal Mach Intell 35(1):39–51

    Article  Google Scholar 

  19. Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233

    Article  Google Scholar 

  20. Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  21. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  22. Xiaohong C, Songcan C, Xue H (2012) A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data. Pattern Recogn 4(5):2005–2018

    MATH  Google Scholar 

  23. Xu D, Yan S, Tao D, Lin S, Zhang H (2007) Marginal Fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE Trans Image Process 16(11):2811–2821

    Article  MathSciNet  Google Scholar 

  24. Yang J, Frangi AF, Yang JY, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  25. You D, Hamsici OC, Martinez AM (2011) Kernel optimization in discriminant analysis. IEEE Trans Pattern Anal Mach Intell 33(3):631–638

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. This work has been supported by PhD research startup foundation of Shenyang Aerospace University(Grant No. 15YB05), Foundation of Liaoning Educational Committee (Grant No. L2015403), Technology Innovation Foundation (Basic Research) of Aviation Industry Corporation of China(Grant No. 2013S60109R), and National Natural Science Foundation of China (Grant No. 61170185, 61303016).

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Correspondence to Zhaokui Li.

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Li, Z., Wang, Y., Zhou, X. et al. Mean Laplacian mappings-based difference LDA for face recognition. Multimed Tools Appl 76, 2243–2265 (2017). https://doi.org/10.1007/s11042-015-3207-z

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  • DOI: https://doi.org/10.1007/s11042-015-3207-z

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