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Compound dictionary learning based classification method with a novel virtual sample generation Technology for Face Recognition

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Abstract

Face recognition has earned its high reputation for many years since its considerable advances. However, it is still faced with the challenge of the small sample size problem. With the inspiration of the axis-symmetrical property of human faces, we propose a novel virtual sample synthesis strategy to address the issue of limited training samples. It is noteworthy that the novel algorithm can produce symmetry-based virtual face images, in which the pixels in symmetric parts of the face image were exchanged. And it is mathematically very tractable and quite easy to implement. In addition, considering the fact that dictionary learning (DL) based classification methods have excellent learning ability, we incorporate the virtual samples to learn virtual dictionary so as to enhance the accuracy of face recognition. Differing from conventional learning algorithms, the proposed method provides new insights into two crucial parts. Firstly, it proposes an originally creative idea and algorithm to automatically generate symmetry-based virtual samples and obtain virtual dictionary. Secondly, the original dictionary and virtual dictionary are integrated to construct the compound dictionary learning based classification in the way of adaptive weighted fusion. In this paper, we take the axis-symmetrical nature of faces into consideration and design a framework to generate compound dictionary, where more satisfactory classification accuracy can be achieved than the original dictionary learning methods, referred as, the locality-constrained and label embedding dictionary learning (LCLE-DL). Moreover, the experimental results demonstrate the superior performance of the proposed method in comparison with state-of-the-art dictionary learning methods.

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References

  1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Process 54(11):4311–4322

    MATH  Google Scholar 

  2. Cai S, Zuo W, Zhang L, Feng X, Wang P (2014) Support vector guided dictionary learning. Computer vision - ECCV 2014 (lecture notes in computer science) 8692:624–639

  3. Du B, Zhang Y, Zhang L, Tao D (2016) Beyond the sparsity-based target detector: a hybrid sparsity and statistics based detector for hyperspectral images. IEEE Trans Image Process 25(11):5345–5357

    MathSciNet  MATH  Google Scholar 

  4. Gong C, Tao D, Liu W, Liu L, Yang J (2017) Label propagation via teaching-to-learn and learning-to-teach. IEEE Transactions on Neural Networks & Learning Systems 28(6):1452–1465

    Google Scholar 

  5. Guo H, Han S, Hao F, Park DS, Min G (2019) SOSP: a stepwise optimal sparsity pursuit algorithm for practical compressed sensing. Multimed Tools Appl 78:3–26

    Google Scholar 

  6. Jiang Z, Lin Z, Davis LS (2013) Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Transactions on Pattern Analysis & Machine Intelligence 35(11):2651–2664

    Google Scholar 

  7. Jung H. C, Hwang B. W, Lee S. W (2004) Authenticating corrupted face image based on noise model. IEEE international conference on automatic face and gesture recognition 272-277

  8. Ke J, Peng Y, Liu S, Wu J, Qiu G (2017) Sample partition and grouped sparse representation. J Mod Opt 64(21):2289–2297

    Google Scholar 

  9. Ke J, Peng Y, Liu S, Li J, Pei Z (2018) Face recognition based on symmetrical virtual image and original training image. J Mod Opt 65(4):367–380

    MathSciNet  Google Scholar 

  10. Khan SA, Hussain A, Usman M, Nazir M, Riaz N, Mirza AM (2014) Robust face recognition using computationally efficient features. Journal of Intelligent & Fuzzy Systems 27(6):3131–3143

    Google Scholar 

  11. Liu H, Xu B, Lu D, et al (2018) A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm. Appl Soft Comput 68:360–376

  12. Liu H, Liu B, Zhang H, et al (2018) Crowd evacuation simulation approach based on navigation knowledge and two-layer control mechanism. Inf Sci 436:247–267

  13. Kim S, Koh K, Lustig M, Boyd S, Gorinevsky D (2007) An interiorpoint method for large-scale L1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1(4):606–617

    Google Scholar 

  14. Li K, Yang J, Jiang J (2015) Nonrigid structure from motion via sparse representation. IEEE Trans Cybernetics 45(8):1401–1413

    Google Scholar 

  15. Li K, Zhu Y, Jiang J, Yang J (2016) Video super-resolution using an Adaptived Superpixel-guided auto-Regeressive model. Pattern Recogn 51(3):59–71

    Google Scholar 

  16. Li Z, Lai Z, Xu Y, Yang J, Zhang D (2017) A locality-constrained and label embedding dictionary learning algorithm for image classification. IEEE Transactions on Neural Networks & Learning Systems 28(2):278–293

    MathSciNet  Google Scholar 

  17. Li L, Peng Y, Qiu G, Sun Z, Liu S (2018) A survey of virtual sample generation technology for face recognition. Artif Intell Rev 50(1):1–20

    Google Scholar 

  18. Li Z, Zhang Z, Qin J, Li S, Cai H (2019) Low-rank analysis–synthesis dictionary learning with adaptively ordinal locality. Neural Netw 119:93–112

    Google Scholar 

  19. Liu S, Peng Y (2012) A local region-based Chan-Vese model for image segmentation. Pattern Recogn 45(7):2769–2779

    MATH  Google Scholar 

  20. Liu T, Tao D (2016) On the performance of Manhattan nonnegative matrix factorization. IEEE Transactions on Neural Networks and Learning Systems 27(9):1851–1863

    MathSciNet  Google Scholar 

  21. Liu S, Peng Y, Ben X, Yang W, Qiu G (2016) A novel label learning algorithm for face recognition. Signal Process 124:141–146

    Google Scholar 

  22. Liu S, Zhang X, Peng Y, Cao H (2016) Virtual images inspired consolidate collaborative representation based classification method for face recognition. J Mod Opt 63(12):1181–1188

    Google Scholar 

  23. Liu Z, Qiu Y, Peng Y, Pu J, Zhang X (2017) Quaternion based maximum margin criterion method for color face recognition. Neural Process Lett 45(3):913–923

    Google Scholar 

  24. Liu S, Li L, Peng Y, Qiu G, Lei T (2017) Improved sparse representation method for image classification. IET Comput Vis 11(4):319–330

    Google Scholar 

  25. Liu S, Li L, Jin M, Hou S, Peng Y (2019) An optimized coefficient vector and representation based classification methods for face recognition. IEEE Access 8:8668–8674. https://doi.org/10.1109/ACCESS.2019.2960928

    Article  Google Scholar 

  26. Liu S, Peng Y, Sun Z, Wang X (2019) Self-calibration of projective camera based on trajectory basis. Journal of Computational Science 31:45–53

    Google Scholar 

  27. Liu Z, Wang J, Liu G, Zhang L (2019) Discriminative low-rank preserving projection for dimensionality reduction. Appl Soft Comput 85:105768

    Google Scholar 

  28. Liu Z, Lai Z, Ou W, Zhang K, Zheng R (2020) Structured optimal graph based sparse feature extraction for semi-supervised learning. Signal Process 170:107456. https://doi.org/10.1016/j.sigpro.2020.107456

    Article  Google Scholar 

  29. Lu C, Shi J, Jia J (2014) Scale adaptive dictionary learning. IEEE Trans Image Process 23(2):837–847

    MathSciNet  MATH  Google Scholar 

  30. Ma L, Moisan L, Yu J, Zeng T (2013) A dictionary learning approach for poisson image deblurring. IEEE Trans Med Imaging 32(7):1277–1289

    Google Scholar 

  31. Peng Y, Liu S, Lei T, Li J, Guo M (2018) Negative ε dragging technique for pattern classification. IEEE Access 6(1):488–494

    Google Scholar 

  32. Peng Y, Li L, Liu S, Li J, Wang X (2018) Extended sparse representation based classification method for face recognition. Mach Vis Appl 29(6):991–1007

    Google Scholar 

  33. Peng Y, Li L, Liu S, Lei T, Wu J (2018) A new virtual samples-based CRC method for face recognition. Neural Process Lett 48:313–327

    Google Scholar 

  34. Peng Y, Li L, Liu S, Wang X, Li J (2018) Weighted constraint based dictionary learning for image classification. Pattern Recogn Lett 130:99–106. https://doi.org/10.1016/j.patrec.2018.09.008

    Article  Google Scholar 

  35. Peng Y, Sehdev P, Liu S, Li J, Wang X (2018) l2,1-norm minimization based negative label relaxation linear regression for feature selection. Pattern Recogn Lett 116:170–178

    Google Scholar 

  36. Peng Y, Li L, Liu S, Lei T (2018) Space-frequency domain based joint dictionary learning and collaborative representation for face recognition. Signal Process 147:101–109

    Google Scholar 

  37. Peng Y, Ke J, Liu S, Li J, Lei T (2019) An improvement to linear regression classification for face recognition. Int J Mach Learn Cybern 10(9):2229–2243

    Google Scholar 

  38. Peng Y, Li L, Liu S, Li J (2019) Virtual samples and sparse representation based classification algorithm for face recognition. IET Comput Vis 13(2):172–177

    Google Scholar 

  39. Peng Y, Liu S, Qian Y et al (2019) A local mean and variance active contour model for biomedical image segmentation. Journal of Computational Science 33:11–19

    MathSciNet  Google Scholar 

  40. Peng Y, Liu S, Wang X, Wu X (2020) Joint local constraint and fisher discrimination based dictionary learning for image classification. Neurocomputing 398:505–519

  41. Peng Y, Zhang L, Liu S, Wu X, Zhang Y, Wang X (2019) Dilated residual networks with symmetric skip connection for image denoising. Nuerocomputing 345:67–76

    Google Scholar 

  42. Phillips P, Moon H, Rizvi S, Rauss P (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis & Machine Intelligence 22(10):1090–1104

    Google Scholar 

  43. Sadeghi M, Babaie-Zadeh M, Jutten C (2014) Learning overcomplete dictionaries based on atom-by-atom updating. IEEE Trans Signal Process 62(4):883–891

    MathSciNet  MATH  Google Scholar 

  44. Wang H, Nie F, Cai W, Huang H (2013) Semi-supervised robust dictionary learning via efficient l-norms minimization. Proc. IEEE Int Conf Comput Vis 1145-1152

  45. Wang H, Tu C, Chiang C (2019) Sparse representation for image classification via paired dictionary learning. Multimed Tools Appl 78(12):16945–16963

    Google Scholar 

  46. Wen J, Xu Y, Li Z, Ma Z, Xu Y (2018) Inter-class sparsity based discriminative least square regression. Neural Netw 102:36–47

    MATH  Google Scholar 

  47. Wu S, Cao J (2014) ‘Symmetrical face’ based improved LPP method for face recognition. Optik - International Journal for Light and Electron Optics 125(14):3530–3533

    Google Scholar 

  48. Xu Y, Lu Y (2015) Adaptive weighted fusion: a novel fusion approach for image classification. Neurocomputing 168:566–574

    Google Scholar 

  49. Xu Y, Zhang D, Yang J, Yang J (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Transactions on Circuits and Systems for Video Technology 21(9):1255–1262

    MathSciNet  Google Scholar 

  50. 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(4):1151–1158

    Google Scholar 

  51. Xu Y, Fang X, Li X, Yang J, You J, Liu H, Teng S (2014) Data uncertainty in face recognition. IEEE Transactions on Cybernetics 44(10):1950–1961

    Google Scholar 

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

    Google Scholar 

  53. Xu Y, Zhang Z, Lu G, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recogn 54:68–82

    Google Scholar 

  54. Xu C, Liu T, Tao D, Xu C (2016) Local rademacher complexity for multi-label learning. IEEE Trans Image Process 25(3):1495–1507

    MathSciNet  MATH  Google Scholar 

  55. Xu Y, Li Z, Zhang B, Yang J, You J (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition. Inf Sci 375:171–182

    Google Scholar 

  56. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. International Conference on Computer Vision 2011:543–550

  57. Yang J, Li K, Li K, Lai Y (2015) Sparse non-rigid registration of 3D shapes. Computer Graphics Forum 34(5):89–99

    Google Scholar 

  58. Yang Y, Li B, Li P, Liu Q (2019) A two-stage clustering based 3D visual saliency model for dynamic scenarios. IEEE Transactions on Multimedia 21(4):809–820

    Google Scholar 

  59. Yang Y, Liu Q, He X, Liu Z (2019) Cross-view multi-lateral filter for compressed multi-view depth video. IEEE Transaction on Image Processing 28(1):302–315

    MathSciNet  MATH  Google Scholar 

  60. Zhang Q, Li B (2010) Discriminative K-SVD for dictionary learning in face recognition. Proc IEEE Conf Comput Vis Pattern Recog (CVPR) 2691–2698

  61. Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:90–530

    Google Scholar 

  62. Zhang K, Peng Y, Liu S (2018) Discriminative face recognition via kernel sparse representation. Multimed Tools Appl 77(24):32243–32256

    Google Scholar 

  63. Zhang G, Zou W, Zhang X, Zhao Y (2018) Singular value decomposition based virtual representation for face recognition. Multimed Tools Appl 77:7171–7186

    Google Scholar 

  64. Zhu X, Ben X, Liu S, Yan R, Meng W (2018) Coupled source domain targetized with updating tag vectors for micro-expression recognition. Multimed Tools Appl 77(3):3105–3124

    Google Scholar 

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No.61873155, 61672333), Transfer and Promotion Plan of Scientific and Technological Achievements of Shaanxi Province (No.2019CGXNG-019), the National Natural Science Foundation of Shaanxi Province (No.2018JM6050), the Key Science and Technology Program of Shaanxi Province, (No.2016GY-081).

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Authors and Affiliations

  1. Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, 710062, China

    Liping Li, Yali Peng & Shigang Liu

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Liping Li, Yali Peng & Shigang Liu

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  1. Liping Li
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Correspondence to Yali Peng.

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Li, L., Peng, Y. & Liu, S. Compound dictionary learning based classification method with a novel virtual sample generation Technology for Face Recognition. Multimed Tools Appl 79, 23325–23346 (2020). https://doi.org/10.1007/s11042-020-08965-9

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