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Joint collaborative representation algorithm for face recognition

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Abstract

Collaborative representation is well known owing to its good performance in classification, especially classification on high-dimensional data. Collaborative representation does very well in classification problems of high-dimensional data, e.g., images classification. In this paper, we point out that conventional algorithm for collaborative representation does not well exert its potential. Our analysis shows that frequency-domain features of images provide good representations of objects and joint of frequency-domain features and space-domain features enables collaborative representation to perform very well in face recognition. The circular symmetry of the used frequency-domain features is exploited to design an efficient procedure for recognition of faces in the frequency domain. The setting procedure of the adaptive weight is also impressing because it can obtain reasonable weights for the two classifiers on two groups of data. It properly uses reliability of the data as weight of the corresponding classifier. The proposed joint collaborative representation algorithm achieves better result than conventional algorithm.

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References

  1. Morel JM, Petro AB, Sbert C (2014) Screened poisson equation for image contrast enhancement. Image Process On Line 4:16–29

    Article  Google Scholar 

  2. Huang K, Wu Z, Wang Q (2005) Image enhancement based on the statistics of visual representation. Image Vis Comput 23(1):51–57

    Article  Google Scholar 

  3. Ke WM, Chen CR, Chiu CT (2011) BiTA/SWCE: image enhancement with bilateral tone adjustment and saliency weighted contrast enhancement. IEEE Trans Circuits Syst Video Technol 21(3):360–364

    Article  Google Scholar 

  4. Schrijver DD, Sutter RD, Lambert P, Walle RVD (2005) Lossless image coding based on fractals. In: 7th IASTED International Conference on Signal and Image Processing, pp 51–56

  5. Ha H-Y (2015) Integrating deep learning with correlation-based multimedia semantic concept detection. FIU Electronic Theses and Dissertations, 2268

  6. Xu Y, Fang X, Wu J, Li X, Zhang D (2015) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):1–1

    MathSciNet  MATH  Google Scholar 

  7. Ngiam J, Khosla A, Kim M, Nam J, Lee H, Ng AY (2011) Multimodal deep learning. In: International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28–July 2011, pp 689–696

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

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

    Article  Google Scholar 

  10. Sun F, Tang J, Li H, Qi GJ, Huang TS (2014) Multi-label image categorization with sparse factor representation. IEEE Trans Image Process A Publ IEEE Signal Process Soc 23(3):1028–37

    MathSciNet  MATH  Google Scholar 

  11. Yong X, Zhong Z, Jian Y, You J (2016) A new discriminative sparse representation method for robust face recognition via \(l_2\) regularization. IEEE Trans Neural Netw Learn Syst 1–10

  12. Zhu P, Zuo W, Zhang L, Shiu CK (2013) Image set-based collaborative representation for face recognition. IEEE Trans Inf Forensics Secur 9(7):1120–1132

    Google Scholar 

  13. Yang X, Wang B, Li YR, He T (2015) Robust landmark-based image registration using \(l_1\) and \(l_2\) norm regularizations. In: IEEE International Conference on Bioinformatics and Biomedicine, pp 425–428

  14. Xu Y, Zhang D, Yang J, Yang JY (2011) A two-phase test sample sparse representation method for use with face recognition. IEEE Trans Circuits Syst Video Technol 21(9):1255–1262

    Article  MathSciNet  Google Scholar 

  15. Zhang L, Shen Y, Li H, Lu J (2015) 3D palmprint identification using block-wise features and collaborative representation. IEEE Trans Pattern Anal Mach Intell 37(8):1730–1736

    Article  Google Scholar 

  16. Xu Y, Zhu Q, Fan Z, Zhang D, Mi J, Lai Z (2013) Using the idea of the sparse representation to perform coarse-to-fine face recognition. Inf Sci 238(7):138–148

    Article  MathSciNet  Google Scholar 

  17. orel M, roubek F (2016) Fast convolutional sparse coding using matrix inversion lemma. Digit Signal Process 55:44–51

    Article  Google Scholar 

  18. Xu N, Jiang A, Zhou L, Tang Y, Chen Y (2016) Image denoising via sparse coding using eigenvectors of graph Laplacian. Digit Signal Process 50:114–122

    Article  Google Scholar 

  19. Mansano A, Matsuoka JA, Afonso LCS, Papa JP, Faria F, Torres RDS (2012) Improving image classification through descriptor combination. In: Sibgrapi Conference on Graphics, Patterns and Images, pp 324–329

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

    Article  Google Scholar 

  21. Hong X, Zhao G, Pietikainen M, Chen X (2014) Combining LBP difference and feature correlation for texture description. IEEE Trans Image Process A Publ IEEE Signal Process Soc 23(6):2557–68

    Article  MathSciNet  MATH  Google Scholar 

  22. Chen J, Shan S, He C, Zhao G, Pietikinen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  23. Long G, Kneip L, Li X., Zhang X (2015) Simplified mirror-based camera pose computation via rotation averaging. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  24. 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 Recognit 54(C):68–82

    Article  Google Scholar 

  25. Zhang D, Xu Y, Zuo W (2016) Discriminative learning in biometrics. Springer, Berlin

    Book  Google Scholar 

  26. Xu Y, Zhu Q (2013) A simple and fast representation-based face recognition method. Neural Comput Appl 22:1543. https://doi.org/10.1007/s00521-012-0833-5

    Article  Google Scholar 

  27. Saha T, Rangwala H, Domeniconi C (2014) FLIP: active learning for relational network classification. In: Calders T, Esposito F, Meo R (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science, vol 8726. Springer, Berlin

    Google Scholar 

  28. Liu X, Lingyun L, Shen Z, Kaixuan L (2018) A novel face recognition algorithm via weighted kernel sparse representation. Future Gener Comput Syst 28:653–663

    Article  Google Scholar 

  29. Zhang W, Zhang Y, Ma L (2015) Multimodal learning for facial expression recognition. Pattern Recognit 48(10):3191–3202

    Article  Google Scholar 

  30. Khaleghi B, Khamis A, Karray FO, Razavi SN (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fus 14(1):28–44

    Article  Google Scholar 

  31. Chen B, Zhang W, Hu G, Yu L (2015) Networked fusion Kalman filtering with multiple uncertainties. IEEE Trans Aerosp Electron Syst 51(3):2232–2249

    Article  Google Scholar 

  32. Yu L, Xia BY, Wang X, Lou XW (2015) Adaptive weighted fusion: a novel fusion approach for image classification. Neurocomputing 168:566–574

    Article  Google Scholar 

  33. Xu Y, Li X, Yang J, Zhang D (2014) Integrate the original face image and its mirror image for face recognition. Neurocomputing 131(7):191–199

    Article  Google Scholar 

  34. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  35. Tian C, Zhang Q, Sun G et al (2018) FFT consolidated sparse and collaborative representation for image classification. Arab J Sci Eng 43:741. https://doi.org/10.1007/s13369-017-2696-7

    Article  Google Scholar 

  36. Xu Y, Zhu X, Li Z (2013) Using the original and symmetrical face training samples to perform representation based two-step face recognition. Pattern Recognit 46(4):1151–1158

    Article  Google Scholar 

  37. http://www.itl.nist.gov/iad/humanid/feret/feret_master.html

  38. http://www.face-rec.org/databases/

  39. Yang J, Yang J, Frangi AF (2003) Combined fisherfaces framework. Image Vis Comput 21(12):1037–1044

    Article  Google Scholar 

  40. Yong X, Zhang Z, Guangming L, Yang J (2016) Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognit 54:68–82

    Article  Google Scholar 

  41. http://www.anefian.com/face_reco.htm

  42. Wang Y, Tang Y, Li L (2015) Robust face recognition via minimum error entropy based atomic representation. IEEE Trans Image Process 24(12):5868–5878

    Article  MATH  Google Scholar 

  43. Fuxing Z, Tao Z, Rui W (2017) Gbest-guided covariance matrix adaptation evolution strategy for large scale global optimization. In: Huang DS, Bevilacqua V, Premaratne P, Gupta P (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science, vol 10361. Springer, Cham

    Google Scholar 

  44. Chi H, Xia H, Tang X et al (2017) Supervised neighborhood regularized collaborative representation for face recognition. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4851-2

    Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of Guangdong, China (No. 2016A030313023), and Shenzhen Science and Technology Program under Grant (No. JCYJ20160322114027138).

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Authors and Affiliations

  1. School of Computer Engineering, Shenzhen Polytechnic, Shenzhen, 518055, People’s Republic of China

    Xincan Fan, Kaiyang Liu & Haibo Yi

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  1. Xincan Fan
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  2. Kaiyang Liu
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  3. Haibo Yi
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Correspondence to Haibo Yi.

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Fan, X., Liu, K. & Yi, H. Joint collaborative representation algorithm for face recognition. J Supercomput 75, 2304–2314 (2019). https://doi.org/10.1007/s11227-018-2606-0

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