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A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation


Proposed is a simple yet fast and robust approach to face recognition. This approach is developed specifically to address the challenges due to variations of illumination, expression and occlusion, when studying the facial images of a large population. The proposed approach exploits an improved collaborative representation algorithm. First, we construct an initial dictionary by extracting the multi-scale and multi-orientation Gabor features of the image. Second, we design a new discriminative dictionary by an improved K-SVD algorithm, so that the query sample can be better represented for classification. Finally, l 2-norm of coding residual is calculated by collaborative representation based classification with regularized least square method (CRC-RLS) and the class of test sample is obtained. Experiments on two benchmark face datasets show that the proposed method can achieve high classification accuracy and is comparatively low in terms of time-consumption, compared to the CRC-RLS algorithm.

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  1. 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 

  2. Lu CY, Min H, Gui J, Zhu L, Lei Y (2013) Face recognition via weighted sparse representation. J Vis Commun Image Represent 24(2):111–116

    Article  Google Scholar 

  3. Ortiz EG, Becker B (2014) Face recognition for web-scale datasets. Comput Vis Image Underst 118:153–170

    Article  Google Scholar 

  4. Shu X, Gao Y, Lu H (2012) Efficient linear discriminant analysis with locality preserving for face recognition. Pattern Recogn 45(5):1892–1898

    Article  MATH  Google Scholar 

  5. Yang M, Zhang L, Shiu SC, Zhang D (2013) Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary. Pattern Recogn 46(7):1865–1878

    Article  Google Scholar 

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

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

    Article  Google Scholar 

  8. Zhang L, Yang M, Feng X et al (2012) Collaborative representation based classification for face recognition. arXiv preprint arXiv:1204.2358

    Google Scholar 

  9. Yang M, Feng Z, Shiu SC, Zhang L (2014) Fast and robust face recognition via coding residual map learning based adaptive masking. Pattern Recogn 47(2):535–543

    Article  MATH  Google Scholar 

  10. Jiang ZL, Lin Z, Larry SD (2013) Label consistent k-svd: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664

    Article  Google Scholar 

  11. Zhang Q, Li B (2010) Discriminative K-SVD for dictionary learning in face recognition[C]//computer vision and pattern recognition (CVPR). IEEE Conference on IEEE, 2010, pp 2691–2698

  12. Ou W, You X, Tao D et al (2014) Robust face recognition via occlusion dictionary learning. Pattern Recogn 47(4):1559–1572

    Article  Google Scholar 

  13. Schiele B, Crowley JL (2000) Recognition without correspondence using multidimensional receptive field histograms. Int J Comput Vis 36(1):31–50

    Article  Google Scholar 

  14. Daugman JG (1998) Complete discrete 2d Gabor transforms by neural networks for image-analysis and compression. IEEE Trans Acoust Speech Signal Process 36(7):1169–1179

    Article  MATH  Google Scholar 

  15. Kyrki V, Kamarainen JK, Kalviainen H (2004) Simple Gabor feature space for invariant object recognition. Pattern Recogn 25(3):311–318

    Article  Google Scholar 

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Correspondence to Zhigang Jin.

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Cheng, Y., Jin, Z., Chen, H. et al. A fast and robust face recognition approach combining Gabor learned dictionaries and collaborative representation. Int. J. Mach. Learn. & Cyber. 7, 47–52 (2016).

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  • Face recognition
  • Gabor dictionary
  • Dictionary learning
  • Collaborative representation