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Discriminant sparse locality preserving projection for face recognition

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Abstract

In this paper, aiming at the drawback of the popular dimensionality reduction method Discriminant Sparse Neighborhood Preserving Embedding(DSNPE), i.e. the construction of its between-class scatter is too complexity, a novel algorithm Discriminant Locality Preserving Projection (DSLPP) is proposed. Our proposal retains the local sparse reconstructive relationships of DSNPE and constructs a novel between-class scatter by using all mean faces as sparse representation dictionary. In particular, DSLPP preserves the sparse reconstructive relationship of mean face, so then it can not only efficiently reduce the between-class scatter computation complexity of DSNPE, but also increase the discriminant power. In the experiment, we compare DSLPP with classic and state-of-the-art dimensionality reduction methods on the publicly available data sets such as ORL, Yale, UMIST, and AR, and further apply the Gabor feature into DSLPP on AR face database to further improve its performance. The experimental results show that DSLPP is able to obtain a better representation of the class information and achieve much higher recognition accuracy.

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References

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  3. Chen HT, Chang HW, Liu TL (2005) Local discriminant embedding and its variants. In: Proceedings of the IEEE computer visual and pattern recognition (CVPR), pp 846– 853

  4. Cheng B, Yang J, Yan S, Fu Y, Huang T (2010) Learning with L1-graph for image analysis. IEEE Trans Image Process 19(4):858–866

    Article  MathSciNet  MATH  Google Scholar 

  5. Chun D, Shi-Lin Z, Ji-Xiang S, Hao S, Liang-Liang W (2013) Discriminant embedding by sparse representation and nonparametric discriminant analysis for face recognition. J Cent South Univ 20:3564–3572

    Article  Google Scholar 

  6. Cox TF, Cox MAA (2000) Multidimensional Scaling, 2nd edn. Chapman and hall/CRC

  7. Graham DB, Allinson NM (1998) Characterizing virtual eigensignatures for general purpose face recognition. In: Face recognition: From theory to applications, vol 163, pp 446– 456

  8. Gui J, Sun Z, Jia W, Hu R, Lei Y, Ji S (2012) Discriminant Sparse Neighborhood Preserving Embedding for Face Recognition. Pattern Recogn 45 (8):2884–2893

    Article  MATH  Google Scholar 

  9. He X, Niyogi P (2003) Locality preserving projections. In: Proceedings of Conference on Advances in Neural Information Processing Systems (NIPS)

  10. He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1208–1213

  11. He X, Yan S, Hu Y, Niyogi P, Zhang H-J (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  12. Huang P, Tang Z, Chen C, Yang Z (2014) Local maximal margin discriminant embedding for face recognition. J Vis Commun Image R 25:296–305

    Article  Google Scholar 

  13. Joia P, Paulovich F, Coimbra D, Cuminato J, Nonato L (2011) Local affine multidimensional projection. IEEE Trans Vis Comput Graph 17(12):2563–2571

    Article  Google Scholar 

  14. Jolliffe IT (1986) Principal component analysis. Springer, New York

    Book  MATH  Google Scholar 

  15. Kong J, Wang S, Wang J, Ma L, Fu B, Lu Y (2009) A novel approach for face recognition based on supervised locality preserving projection and maximum margin criterion. International Conference on Computer Engineering and Technology ICCET 2009(1):419–423

    Google Scholar 

  16. Lai Z, Li Y, Wan M, Jin Z (2013) Local sparse representation projections for face recognition. Neural Comput Applic 23:2231–2239

    Article  Google Scholar 

  17. Li HF, Jiang T, Zhang KS (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157–165

    Article  Google Scholar 

  18. Li W, Ruan Q, Wan J (2014) Dimensionality reduction using graph-embedded probability-based semi-supervised discriminant analysis. Neurocomputing 138:283–296

    Article  Google Scholar 

  19. Lishan Q, Songcan C, Xiaoyang T (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43:331–341

    Article  MATH  Google Scholar 

  20. Lu G-F, Jin Z, Zou J (2012) Face recognition using discriminant sparsity neighborhood preserving embedding. Knowl-Based Syst 31:119–127

    Article  Google Scholar 

  21. Martinez AM, Benavente R (1998) The AR face database, CVC, Univ. Autonoma Barcelona, Barcelona, Spain, Technical Report, 24

  22. Paulovich F, Nonato L, Minghim R, Levkowitz H (2008) Least square projection: a fast high-precision multidimensional projection technique and its application to document mapping. IEEE Trans Visual Comp Graph 14(3):564–575

    Article  Google Scholar 

  23. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3):252–264

    Article  Google Scholar 

  24. Roweis ST, Saul LK (2000) Nonlinear dimension reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  25. See (http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html)

  26. Tenenbaum JB, da Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–2323

    Article  Google Scholar 

  27. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Statist Soc Ser B (Methodological) 58(1):267–288

    MathSciNet  MATH  Google Scholar 

  28. Weinberger KQ, Saul LK (2006) An introduction to nonlinear dimensionality reduction by maximum variance unfolding. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol 2, pp 1683–1686

  29. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31 (2):210–227

    Article  Google Scholar 

  30. Wright J, Ma Y, Mairal J, Sapiro G, Huang T-S, Yan S (2010) Sparse representation for computer vision and pattern recognition. In: Proceedings of the IEEE, Special Issue on Applications of Compressive Sensing and Sparse Representation, vol 98, pp 1031–1044

  31. Wright J, Ma Y, Mairal J, Sapiro G, Huang T, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044

    Article  Google Scholar 

  32. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  33. Yan S, Wang H (2009) Semi-supervised learning by sparse representation. In: SIAM International Conference on Data Mining (SDM), pp 792–801

  34. Yang-Qing J, Fei-Ping N, Chang-Shui Z (2009) Trace ratio problem revisited. IEEE Trans Neural Netw 20(4):729–735

    Article  Google Scholar 

  35. Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. Computer Vision-ECCV:448–461

  36. Yang J, Chu D, Zhang L, Xu Y, Yang J (2013) Sparse representation classifier steered discriminative projection with applications to face recognition. IEEE Trans Neural Netw Learn Syst 24(7):1023–1035

    Article  Google Scholar 

  37. Yang M, Zhang L, shiu S, Zhang D (2013) Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary 46(7):1865–1878

  38. Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vis 109 (3):209–232

    Article  MathSciNet  MATH  Google Scholar 

  39. Yu W, Teng X, Liu C (2006) Face recognition using discriminant locality preserving projections. Image Vis Comput 24(3):239–248

    Article  Google Scholar 

  40. Zhang Z, Zha H (2005) Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J Sci Comput 26(1):313–338

    Article  MathSciNet  MATH  Google Scholar 

  41. Zhao H, Sun S, Jing Z, Yang J (2006) Local structure based supervised feature extraction. Pattern Recogn 39:1546–1550

    Article  MATH  Google Scholar 

  42. Zhao M-B, Zhang Z, Chow TW (2012) Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction. Pattern Recogn 45:1482–1499

    Article  MATH  Google Scholar 

  43. Zang F, Zhang J (2011) Discriminative learning by sparse representation for classification. Neurocomputing 74:2176–2183

    Article  Google Scholar 

  44. Zhen-Yue Z, Jing W, Hong-Yuan Z (2012) Adaptive manifold learning. IEEE Trans Pattern Anal Mach Intell 34(2):253–265

    Article  Google Scholar 

  45. Zhong F, Zhang J, Li D (2014) Discriminant locality preserving projections based on L1-norm maximization. IEEE Transactions on Neural Networks and Learning Systems 25(11):2065–2074

    Article  Google Scholar 

  46. Zhu L, Zhu S (2007) Face recognition based on orthogonal discriminant locality preserving projections. Neurocomputing 70(7-9):1543–1546

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61472297, U1404622 and 61503082.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    Yifang Yang

  2. College of Science, Xi’an Shiyou University, Xi’an, 710065, China

    Yifang Yang

  3. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Yuping Wang

  4. School of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350118, China

    Xingsi Xue

Authors
  1. Yifang Yang
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  2. Yuping Wang
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  3. Xingsi Xue
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Correspondence to Yuping Wang.

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Yang, Y., Wang, Y. & Xue, X. Discriminant sparse locality preserving projection for face recognition. Multimed Tools Appl 76, 2697–2712 (2017). https://doi.org/10.1007/s11042-015-3212-2

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  • DOI: https://doi.org/10.1007/s11042-015-3212-2

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