Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis

Basma Ammour; Toufik Bouden; Larbi Boubchir

Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis

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Author(s): Basma Ammour¹ ; Toufik Bouden² ; Larbi Boubchir³
- Affiliations: 1: Department of Electronics , University of Mohammed Seddik Benyahia , BP 98 , Jijel , Algeria ;
  2: Department of Automatic , University of Mohammed Seddik Benyahia , BP 98 , Jijel , Algeria ;
  3: LIASD Research Lab, University of Paris 8 , 2 rue Liberté , 93526 Saint-Denis , France
Source: Volume 7, Issue 5, September 2018, p. 482 – 489
DOI: 10.1049/iet-bmt.2017.0251 , Print ISSN 2047-4938, Online ISSN 2047-4946

Received 04/11/2017, Accepted 20/02/2018, Revised 16/02/2018, Published 21/02/2018

A multi-modal biometric system is used to verify or identify a person by exploiting information of more than one biometric modality. It combines the strengths of the unimodal biometric system to solve their limitations. This study proposes schemes of multi-modal biometric system based on texture information extracted from face and two iris (left and right) using hybrid level of fusion. Feature extraction is the key step to get a robust recognition system. Multi-resolution two-dimensional Log-Gabor filter combined with spectral regression kernel discriminant analysis is exploited to extract features from both face and iris modalities. These features are used in the fusion and the classification process. The proposed schemes were tested using CASIA Iris Distance database in the verification mode. The experimental results show that the proposed multi-modal biometric system yields attractive performances of up to 0.24% in terms of equal error rate and outperforms the recent similar existing state-of-the-art methods.

References

1. 1)
  - 6. Connaughton, R., Bowyer, K.W., Flynn, P.J.: ‘Fusion of face and iris biometrics’, in Burge, M., Bowyer, K. (Eds.): ‘Handbook of iris recognition’ (Springer, London, 2013), pp. 219–237.
2. 2)
  - 13. Zhang, Z., Wang, R., Pan, K., et al: ‘Fusion of near infrared face and iris biometrics’, in Lee, S.W., Li, S.Z. (Eds.): ‘Advances in biometrics’ (Springer, Berlin, Heidelberg, 2007), pp. 172–180.
3. 3)
  - 9. Eskandari, M., Toygar, Ö.: ‘Fusion of face and iris biometrics using local and global feature extraction methods’, Signal, Image Video Process., 2014, 8, (6), pp. 995–1006.
4. 4)
  - 17. Eskandari, M., Toygar, Ö.: ‘Selection of optimized features and weights on face-iris fusion using distance images’, J. Comput. Vis. Image Underst., 2015, 137, pp. 63–75.
5. 5)
  - 15. Zhang, X., Sun, Z., Tan, T.: ‘Hierarchical fusion of face and iris for personal identification’. Proc. Int. Conf. Pattern Recognition, Istanbul, Turkey, August 2010, pp. 217–220.
6. 6)
  - 1. Ghayoumi, M.: ‘A review of multimodal biometric systems: fusion methods and their applications’. Proc. Int. Conf. Computer and Information Science, Las Vegas, NV, USA, July 2015.
7. 7)
  - 3. Eskandari, M., Toygar, Ö.: ‘Score level fusion for face–iris multimodal biometric system’, in Gelenbe, E., Lent, R., (Eds.): ‘Information sciences and systems’ (Springer International Publishing, Switzerland, 2013), pp. 199–208.
8. 8)
  - 22. Wang, Q., Zhu, B., Liu, Y., et al: ‘Iris-face fusion and security analysis based on fisher discriminant’, Int. J. Smart Sens. Intell. Syst., 2015, 8, (1), pp. 387–407.
9. 9)
  - 8. Liau, H.F., Isa, D.: ‘Feature selection for support vector machine-based face–iris multimodal biometric system’, Expert Syst. Appl., 2011, 38, (9), pp. 11105–11111.
10. 10)
  - 25. Bounneche, M.D., Boubchir, L., Bouridane, A., et al: ‘Multi-spectral palmprint recognition based on oriented multiscale Log-Gabor filters’, Neurocomputing., 2016, 205, (12), pp. 274–286.
11. 11)
  - 16. Morizet, N., Gilles, J.: ‘A new adaptive combination approach to score level fusion for face and iris biometrics combining wavelets and statistical moments’, in Bebis, G., et al (Eds.): ‘Advances in visual computing’ (Springer, Berlin, Heidelberg, 2008), pp. 661–671.
12. 12)
  - 10. Rattani, A., Tistarelli, M.: ‘Robust multi-modal and multi-unit feature level fusion of face and iris biometrics’, in Tistarelli, M., Nixon, M.S. (Eds.): ‘Advances in biometrics’ (Springer, Berlin, Heidelberg, 2009), pp. 960–969.
13. 13)
  - 12. Huo, G., Liu, Y., Zhu, X., et al: ‘Face–iris multimodal biometric scheme based on feature level fusion’, J. Electron. Imaging, 2015, 24, (6), pp. 063020–063020.
14. 14)
  - 11. Son, B., Lee, Y.: ‘Biometric authentication system using reduced joint feature vector of iris and face’, in Kanade, T., Jain, A., Ratha, N.K. (Eds.): ‘Audio- and video-based biometric person authentication’ (Springer, Berlin, Heidelberg, 2005), pp. 513–522.
15. 15)
  - 29. Jain, A.K., Duin, R.P.W., Mao, J.: ‘Statistical pattern recognition: a review’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, (1), pp. 4–37.
16. 16)
  - 19. Miao, D., Zhang, M., Sun, Z., et al: ‘Bin-based classifier fusion of iris and face biometrics’, Neurocomputing, 2017, 224, pp. 105–118.
17. 17)
  - 5. Eskandari, M., Sharifi, O.: ‘Optimum scheme selection for face-iris biometric’, IET Biometrics, 2017, 6, (5), pp. 334–341.
18. 18)
  - 26. Cai, D., He, X., Han, J.: ‘Speed up kernel discriminant analysis’, Int. J. Very Large Data Bases, 2011, 20, (1), pp. 21–33.
19. 19)
  - 20. Sharifi, O., Eskandari, M.: ‘Optimal face-iris multimodal fusion scheme’, Symmetry, 2016, 8, (6), p. 48.
20. 20)
  - 2. Sim, H.M., Asmuni, H., Hassan, R., et al: ‘Multimodal biometrics: weighted score level fusion based on non-ideal iris and face images’, Expert Syst. Appl., 2014, 41, (11), pp. 5390–5404.
21. 21)
  - 21. Murphy, T.M., Broussard, R., Schultz, R., et al: ‘Face detection with a Viola-Jones based hybrid network’, IET Biometrics, 2017, 6, (3), pp. 200–210.
22. 22)
  - 7. Ross, A., Jain, A.: ‘Information fusion in biometrics’, Pattern Recognit. Lett., 2003, 24, (13), pp. 2115–2125.
23. 23)
  - 14. Wang, Y., Tan, T., Jain, A.K.: ‘Combining face and iris biometrics for identity verification’, in Kittler, J., Nixon, M.S. (Eds.): ‘Audio- and video-based biometric person authentication’ (Springer, Berlin, Heidelberg, 2003), pp. 805–813.
24. 24)
  - 27. Elnasir, S., Shamsuddin, S.M., Farokhi, S.: ‘Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis’, J. Electron. Imaging, 2015, 24, (1), pp. 13031–13031.
25. 25)
  - 28. ‘Biometrics ideal test’. 2013. Available at http://biometrics.idealtest.org/dbDetailForUser.doid=4.
26. 26)
  - 23. Wang, Z., Wang, E., Wang, S., et al: ‘Multimodal biometric system using face-iris fusion feature’, J. Comput., 2011, 6, (5), pp. 931–938.
27. 27)
  - 18. Khiari-Hili, N., Montagne, C., Lelandais, S., et al: ‘Quality dependent multimodal fusion of face and iris biometrics’. Proc. Int. Conf. Image Processing Theory Tools and Applications (IPTA), Oulu, Finland, 2016, pp. 1–6.
28. 28)
  - 24. Yingzi, D.: ‘Using 2D Log-Gabor spatial filters for iris recognition’. Proc. Int. Conf. Biometric Technology for Human Identification, Florida, USA, April 2006.
29. 29)
  - 4. Deshmukh, S., Patwardhan, M., Mahajan, A.: ‘Survey on real-time facial expression recognition techniques’, IET Biometrics, 2016, 5, (3), pp. 155–163.

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Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis

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