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Manifold sparsity preserving projection for face and palmprint recognition

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Abstract

Sparsity Preserving Projection (SPP) has been recently successfully applied on pattern recognition applications and is the basis for a series of follow up extensions. However, being unsupervised for dimensionality reduction, SPP does not employ the discriminative information of class labels when projecting data into a smaller subspace. This paper proposes a manifold sparsity learning method called Manifold Sparsity Preserving Projection (MSPP) for the face and palmprint recognition. Our method employs the manifold structure for better preserving the sparsity of data in the embedding space. Differing from recent localized sparsity learning methods such as Local Sparse Representation Projections (LSRP) and Local Sparse Preserving Projections (LSPP), which enforce a one-to-one matching between a sample and its sparsely reconstructed model, our method employs manifold data structure to ensure that a sample and all its classmate’s sparsely reconstructed models remain as close as possible in the new space. We show that when manifold and sparsity information are simultaneously accounted for, their discriminative power is significantly leveraged. An immediate bonus of our approach is that there is no tuning parameter whatsoever for performance variation. We analytically demonstrate the aforementioned features of our approach and then, using a series of experiments on ORL, Yale, IIT Delhi near infrared facial, and NIR set of PolyU Multispectral palmprint databases its pragmatic consequences will be pictured.

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References

  1. Asif MS, Romberg J (2014) Sparse recovery of streaming signals using L1-homotopy. IEEE Trans Signal Process 62(16):4209–4223

    Article  MathSciNet  Google Scholar 

  2. Belhumeour 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 

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

    Article  MATH  Google Scholar 

  4. Cai D, He X, Han J, Zhang HJ (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614

    Article  Google Scholar 

  5. Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In: Proceedings of the 20rd International Joint Conference on Artificial Intelligence (IJCAI), pp 708–713

  6. Cheng H (2015) Sparse representation, modeling and learning in visual recognition. Theory, algorithms and applications, advances in computer vision and pattern recognition, Springer

  7. Cheng B, Yang JC, Yan SC, Fu Y, Huang TS (2010) Learning with l(1)-graph for image analysis. IEEE Trans Image Process 19(4):858–866

    Article  MathSciNet  MATH  Google Scholar 

  8. Cui J, Wen J, Fan Z (2014) Appearance-based bidirectional representation for palmprint recognition. Multimed Tools Appl 74(24):10989–11001

    Article  Google Scholar 

  9. Feng G, Hu D, Zhou Z (2008) A direct locality preserving projections (DLPP) algorithm for image recognition. Neural Process Lett 27:247–255

    Article  Google Scholar 

  10. Gao Q, Huang Y, Zhang H, Hong X, Li K, Wang Y (2015) Discriminative sparsity preserving projections for image recognition. Pattern Recogn. doi:10.1016/j.patcog.2015.02.015

  11. Gong PH, Zhang CS, Lu ZS, Huang JH, Ye JP (2013) A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems. In: Proceedings of International Conferenceon Machine Learning (ICML), pp 37–45

  12. Gui J, Sun Z, Jia W, Hub R, Lei Y, Ji S (2012) Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recogn 45(8):2884–2893

    Article  MATH  Google Scholar 

  13. He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using laplacian faces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  14. He X, Cai D, Yan S, Zhang HJ (2005) Neighborhood preserving embedding. In: International Conference Computer Vision (ICCV), pp 1208-1213

  15. Ibrahim S, Jaafar H, Ramli DA (2014), Robust palm print verification system based on evolution of kernel principal component analysis. International Conference on Control System, Computing and Engineering (ICCSCE), pp 202–207

  16. IIT Delhi Near IR Face Database. Indian Institute of Technology Delhi.www4.comp.polyu.edu.hk/~csajaykr/IITD/FaceIR.htm. Accessed 28 July 2016

  17. Jing X, Li S, Zhu S, Liu Q, Yang J, Lu J (2011), Supervised local sparsity preserving projection for face feature extraction. Asian Conference on Pattern Recognition (ACPR), pp 555–559

  18. Joliffe I (1986) Principal component analysis. Springer, New York

    Book  Google Scholar 

  19. Kumar A, Srikanth T (2008) Online personal identification in night using multiple face representations. In: International Conference on Pattern Recognition (ICPR), pp 1–4

  20. Lai Z, Li Y, Wan M, Jin Z (2013) Local sparse representation projections for face recognition. Neural Comput & Applic 23(7–8):2231–2239

    Article  Google Scholar 

  21. Lou S, Zhao X, Chuang Y, Yu H, Zhang S (2015) Graph regularized sparsity discriminant analysis for face recognition. Neurocomputing. doi:10.1016/j.neucom.2015.04.116

  22. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Netw 14(1):117–126

    Article  Google Scholar 

  23. Mika S, Ratsch G, Weston J, Scholkopf B, Muller K-R (1999) Fisher discriminant analysis with kernels. In: Proceedings of the IEEE International Workshop on Neural Networks for Signal Processing, pp 41–48

  24. ORL Face Database. AT&T Laboratories Cambridge. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. Accessed 12 April 2015

  25. PolyU Multispectral Palmprint Database. The Hong Kong Polytechnic University. http://www.comp.polyu.edu.hk/~biometrics/MultispectralPalmprint/MSP.html. Accessed 17 March 2016

  26. Qiao L, Chena S, Tana X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recogn 43(1):331–341

    Article  Google Scholar 

  27. Radovanovic M, Nanopoulos A, Ivanovic M (2010) On the existence of obstinate results in vector space models. In: Proceedings of international ACM SIGIR Conference on Research and Development in Information Retrieval (RDIR), pp 186–193

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

    Article  Google Scholar 

  29. Saxena DK, Deb K (2007) Non-linear dimensionality reduction procedures for certain large dimensional multi-objective optimization problems: employing correntropy and a novel maximum variance unfolding. In: Evolutionary Multi-Criterion Optimization, pp 772–787

  30. Scholkopf B, Solma A, Muller K (1999) Kernel principal component analysis. In: Proceedings of the Advances in Kernel Methods-Support Vector Learning, pp 327–352

  31. Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8:1027–1061

    MATH  Google Scholar 

  32. Tenenbaum JB, De SV, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  33. Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp 586–591

  34. Wang Y, Ruan Q (2006) Kernel fisher discriminant analysis for palmprint recognition. IEEE Pattern Recogn 4:457–460

    Google Scholar 

  35. Wang S, Chen H, Peng X, Zhou C (2011) Exponential locality preserving projections for small sample size problem. Neurocomputing 74(17):3654–3662

    Article  Google Scholar 

  36. Waqas J, Yi Z, Zhang L (2013) Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recogn Lett 34(2):201–208

    Article  Google Scholar 

  37. Wei L, Xu F, Yin J, Wu A (2014) Kernel locality-constrained collaborative representation based discriminant analysis. Knowl-Based Syst 70:212–220

    Article  Google Scholar 

  38. Wong WK, Zhao HT (2012) Supervised optimal locality preserving projection. Pattern Recogn 45(1):186–197

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  40. Xu Y, Zhu X, Li Z, Liu G, Lu Y, Liu H (2013) Using the original and symmetrical face training samples to perform representation based two-step face recognition. Pattern Recogn 46:1151–1158

    Article  Google Scholar 

  41. Xu X, Zhang B, Zhong Z (2015) Multiple representations and sparse representation for image classification. Pattern Recogn Lett 68:9–14

    Article  Google Scholar 

  42. Xu Y, Fang X, You J, Chen Y, Liu H (2015) Noise-free representation based classification and face recognition experiments. Neurocomputing 147:307–314

    Article  Google Scholar 

  43. Yale Face Database. Yale University. http://www.cvc.yale.edu/projects/yalefaces/yalefaces.html. Accessed 14 February 2015

  44. 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 

  45. Yang J, Zhang D, Yang JY, Niu B (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664

    Article  Google Scholar 

  46. 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 

  47. Yang Y, Wang Y, Xue X (2016) Discriminant sparse locality preserving projection for face recognition. Multimed Tools Appl. doi:10.1007/s11042-015-3212-2

  48. Yin F, Jiao LC, Shang F, Wang S, Hou B (2013) Fast fisher sparsity preserving projections. Neural Comput & Applic 23(3):691–705

    Article  Google Scholar 

  49. Yu G, Peng H, Wei J, Ma Q (2011) Enhanced locality preserving projections using robust path based similarity. Neurocomputing 74(4):598–605

    Article  Google Scholar 

  50. Zhang D, Kong WK, You J, Wong M (2003) Online Palmprint Identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050

    Article  Google Scholar 

  51. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: International Conference on Computer Vision (ICCV), pp 471–478

  52. Zhang LM, Chen S, Qiao L (2012) Graph optimization for dimensionality reduction with sparsity constraints. Pattern Recogn 45(3):1205–1210

    Article  MATH  Google Scholar 

  53. Zhang Y, Xiang M, Yang B (2015) Linear dimensionality reduction based on hybrid structure preserving projections. Neurocomputing. doi:10.1016/j.neucom.2015.07.011

  54. Zheng Z, Yang F, Tan W, Jia J, Yang J (2007) Gabor feature-based face recognition using supervised locality preserving projection. Signal Process 87(24):73–83

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electronic Engineering, Faculty of Engineering, Lorestan University, Khoramabad, Iran

    Mohsen Tabejamaat & Abdolmajid Mousavi

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  1. Mohsen Tabejamaat
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  2. Abdolmajid Mousavi
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Correspondence to Abdolmajid Mousavi.

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Tabejamaat, M., Mousavi, A. Manifold sparsity preserving projection for face and palmprint recognition. Multimed Tools Appl 77, 12233–12258 (2018). https://doi.org/10.1007/s11042-017-4881-9

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  • DOI: https://doi.org/10.1007/s11042-017-4881-9

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