Sparse signal representation showed promising results in the field of face recognition in the past few years. An algorithm based on a sparsifying transform is considered. It mainly learns a dictionary that can transform the image into sparse vectors. In the transformation domain, the images of the same class should have similar non-zero coefficients pattern that can be used for identification. The classification process of this method only requires to transform the image and make norm comparisons to determine the class of the image. The proposed method shows a comparable performance with the other known methods in the literature by means of accuracy. A novel method in sparsity-based image identification that uses analysis dictionaries is proposed, unlike the conventional sparsity-based methods. One advantage of the proposed algorithm is the low computational cost of the classification process.

References

1. 1)
  - 8. Naseem, I., Togneri, R., Bennamoun, M.: ‘Linear regression for face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (11), pp. 2106–2112 (doi: 10.1109/TPAMI.2010.128).
2. 2)
  - 36. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (2), pp. 210–227 (doi: 10.1109/TPAMI.2008.79).
3. 3)
  - 8. Dong, B., Mi, X.J.: ‘Trimmed sparse coding for robust face recognition’, Electron. Lett., 2017, 53, (22), pp. 1473–1475 (doi: 10.1049/el.2017.2072).
4. 4)
  - 28. Belhumeur, P., Hespanha, J., Kriegman, D.: ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 711–720 (doi: 10.1109/34.598228).
5. 5)
  - 9. Samaria, F.S., Harter, A.C.: ‘Parameterisation of a stochastic model for human face identification’. Proc. 1994 IEEE Workshop on Applications of Computer Vision, Sarasota, FL, USA, December 1994, pp. 138–142.
6. 6)
  - 10. Martinez, A., Benavente, R.: ‘The AR face database’, Technical Report 24, Computer Vision Center, June 1998.
7. 7)
  - 2. Cortes, C., Vapnik, V.: ‘Support-vector networks’, Mach. Learn., 1995, 20, pp. 273–297.
8. 8)
  - 1. Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: ‘Face recognition: a literature survey’, ACM Comput. Surv., 2003, 35, (4), pp. 399–458 (doi: 10.1145/954339.954342).
9. 9)
  - 107. Georghiades, A., Belhumeur, P., Kriegman, D.: ‘From few to many: illumination cone models for face recognition under variable lighting and pose’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (6), pp. 643–660 (doi: 10.1109/34.927464).
10. 10)
  - 1. Cover, T., Hart, P.: ‘Nearest neighbor pattern classification’, Trans. Inf. Theory, 1967, 13, pp. 21–27 (doi: 10.1109/TIT.1967.1053964).
11. 11)
  - 20. Fisher, R.A.: ‘The use of multiple measurements in taxonomic problems’, Ann. Eugenics, 1936, 7, (2), pp. 179–188 (doi: 10.1111/j.1469-1809.1936.tb02137.x).

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This article has following corresponding article(s):

in brief

Sparsifying transform learning for face image classification

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