access icon free Optimum scheme selection for face–iris biometric

Designing a new dynamic and optimal scheme for face–iris fusion based on the score level, feature level and decision level fusion is considered in this study. Prior to implementing the proposed combined level fusion, several schemes are separately implemented at each level of fusion to investigate the performance improvement of each level of fusion on face and iris modalities. In fact, the optimum scheme is constructed by selecting flexible and dynamic features and scores of face and iris biometrics and then combining the advantages of different levels of fusion. Consequently, the scheme produces a set of fast and flexible features and scores for fusion. On the other hand, the idea of threshold-optimised decisions is used in this study to fuse the optimised decisions of face and iris biometrics. Experimental results on verification rates demonstrate a significant improvement of proposed combined level fusion scheme over unimodal and multimodal fusion methods.

Inspec keywords: optimisation; iris recognition; image fusion; eye; face recognition; feature extraction

Other keywords: optimum scheme selection; face biometrics; feature level fusion; iris biometrics; face–iris biometric; face modalities; combined level fusion; decision level fusion; threshold-optimised decisions; face–iris fusion; dynamic feature selection; iris modalities

Subjects: Optimisation techniques; Sensor fusion; Computer vision and image processing techniques; Image recognition; Optimisation techniques

References

    1. 1)
      • 36. Sahmoud, S.A., Abuhaiba, I.S.: ‘Efficient iris segmentation method in unconstrained environments’, Pattern Recognit., 2013, 46, (12), pp. 31743185.
    2. 2)
      • 33. Al-Maadeed, S., Bourif, M., Bouridane, A., et al: ‘Low-quality facial biometric verification via dictionary-based random pooling’, Pattern Recognit., 2016, 52, pp. 238248.
    3. 3)
      • 42. Tan, C.W., Kumar, A.: ‘A unified framework for automated iris segmentation using distantly acquired face images’, IEEE Trans. Image Process., 2012, 21, (9), pp. 40684079.
    4. 4)
      • 47. Tao, Q., Veldhuis, R.: ‘Threshold-optimized decision-level fusion and its application to biometrics’, Pattern Recognit., 2009, 42, (5), pp. 823836.
    5. 5)
      • 6. Lumini, A., Nanni, L.: ‘Over-complete feature generation and feature selection for biometry’, Expert Syst. Appl., 2008, 35, (4), pp. 20492055.
    6. 6)
      • 38. Karthika, R., Parameswaran, L.: ‘Study of Gabor wavelet for face recognition invariant to pose and orientation’. Proc. of the Int. Conf. on Soft Computing Systems, India, 2016, pp. 501509.
    7. 7)
      • 49. Bucak, S., Jin, R., Jain, A.K.: ‘Multiple kernel learning for visual object recognition: a review’, IEEE Trans. on Pattern Anal. Mach. Intell., 2014, 36, (7), pp. 13541369.
    8. 8)
      • 14. Daugman, J.: ‘Combining multiple biometrics’. Available at http://www.cl.cam.ac.uk/users/jgd1000/combine/combine.html.
    9. 9)
      • 41. Arora, S., Londhe, N.D., Acharya, A.K.: ‘Human identification based on iris recognition for distance images’, Int. J. Comput. Appl., 2012, 45, (16), pp. 3239.
    10. 10)
      • 40. Podder, P., Khan, T.Z., Khan, M.H., et al: ‘An efficient iris segmentation model based on eyelids and eyelashes detection in iris recognition system’. Int. Conf. on IEEE Computer Communication and Informatics (ICCCI), 2015, pp. 17.
    11. 11)
      • 20. Wang, Y., Tan, T., Wang, Y., et al: ‘Combining face and iris biometric for identity verification’. Proc. of 4th Int. Conf. on Audio and Video Based Biometric Person Authentication, 2003, pp. 805813.
    12. 12)
      • 4. Ross, A., Nandakumar, K., Jain, A.K.: ‘Handbook of multibiometrics’ (Springer-Verlag Edition, 2006).
    13. 13)
      • 24. Biometrics Ideal Test. Available at: http://biometrics.idealtest.org/dbDetailForUser.do?id=4.
    14. 14)
      • 45. Sharifi, O., Eskandari, M.: ‘Optimal face–iris multimodal fusion scheme’, Symmetry, 2016, 8, (6), p. pp. 48.
    15. 15)
      • 9. Zhang, D., Jing, X., Yang, J.: ‘Biometric image discrimination (BID) technologies’ (IGP/IRM Press, 2006).
    16. 16)
      • 32. Juefei-Xu, F., Savvides, M.: ‘Multi-class Fukunaga Koontz discriminant analysis for enhanced face recognition’, Pattern Recognit., 2016, 52, pp. 186205.
    17. 17)
      • 34. Frucci, M., Nappi, M., Riccio, D., et al: ‘WIRE: watershed based iris recognition’, Pattern Recognit., 2016, 52, pp. 148159.
    18. 18)
      • 26. Bailly, B.E., Bengio, S., Bimbot, F., et al: ‘The BANCA database and evaluation protocol’. Audio and Video-Based Biometric Person Authentication: Proc. of 4th Int. Conf., AVBPA, Germany, 2003 (LNCS, 2688), pp. 625638.
    19. 19)
      • 16. Wang, F., Han, J.: ‘Multimodal biometric authentication based on score level fusion using support vector machine’, Opto-Electron. Rev., 2009, 17, (1), pp. 5964.
    20. 20)
      • 13. Ponti, M. P.Jr: ‘Combining classifiers: from the creation of ensembles to the decision fusion’. IEEE 24th SIBGRAPI Conf. on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 2011, 2011, pp. 110.
    21. 21)
      • 5. Raghavendra, R., Dorizzi, B., Rao, A., et al: ‘Designing efficient fusion schemes for multimodal biometric system using face and palmprint’, Pattern Recognit., 2011, 44, (5), pp. 10761088.
    22. 22)
      • 30. Lei, Y., Guo, Y., Hayat, M., et al: ‘A two-phase weighted collaborative representation for 3D partial face recognition with single sample’, Pattern Recognit., 2016, 52, pp. 218237.
    23. 23)
      • 43. Sutra, G., Dorizzi, B., Garcia-Salitcetti, S., et al: ‘(2013, April 23) A biometric reference system for iris. OSIRIS version 4.1’.
    24. 24)
      • 25. AT&T Laboratories Cambridge, the ORL Database of Faces (ORL). Available at http://www.camorl.co.uk/facedatabase.html, accessed 2009.
    25. 25)
      • 35. Rankin, D.M., Scotney, B.W., Morrow, P.J., et al: ‘Iris recognition failure over time: the effects of texture’, Pattern Recognit., 2012, 45, (1), pp. 145150.
    26. 26)
      • 8. Eskandari, M., Toygar, Ö., Demirel, H.: ‘Feature extractor selection for face–iris multimodal recognition’, ‘Signal, image and video processing, 2014, 8, (6), pp. 11891198.
    27. 27)
      • 1. Liau, H.F., Isa, D.: ‘Feature selection for support vector machine-based face–iris multimodal biometric system’, Expert Syst. Appl., 2011, 38, pp. 1110511111.
    28. 28)
      • 29. Yildiz, M.Ç., Sharifi, O., Eskandari, M.: ‘Log-Gabor transforms and score fusion to overcome variations in appearance for face recognition’. Int. Conf. on Computer Vision and Graphics, 19–21 September 2016 (LNCS, 9972),.
    29. 29)
      • 12. Morvant, E., Habrard, A., Ayache, S.: ‘Majority vote of diverse classifiers for late fusion’. Joint IAPR Int. Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Berlin Heidelberg, August 2014, pp. 153162.
    30. 30)
      • 18. Eskandari, M., Toygar, Ö., Demirel, H.: ‘A new approach for face–iris multimodal biometric recognition using score fusion’, Int. J. Pattern Recognit. Artif. Intell., 2013, 27, (3), p. 1356004.
    31. 31)
      • 23. Civicioglu, P.: ‘Backtracking search optimization algorithm for numerical optimization problems’, Appl. Math. Comput., 2013, 219, (15), pp. 81218144.
    32. 32)
      • 15. Eskandari, M., Toygar, Ö.: ‘Fusion of face and iris biometrics using local and global feature extraction methods’, ‘Signal, image and video processing’, 2014, 8, (6), pp. 9951006.
    33. 33)
      • 21. Sim, H.M., Asmunia, 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. 53905404.
    34. 34)
      • 48. Boser, B.E., Guyon, I., Vapnik, V.: ‘A training algorithm for optimal margin classifiers’. Proc. of the Fifth Annual Workshop on Computational Learning Theory, 1992, pp. 144152.
    35. 35)
      • 44. Wang, X., Tang, X.: ‘Random sampling for subspace face recognition’, Int. J. Comput. Vis., 2006, 70, (1), pp. 91104.
    36. 36)
      • 46. Eskandari, M., Toygar, Ö.: ‘Score level fusion for face–iris multimodal biometric system’. in Gelenbe, , Erol, , Lent, , Ricardo, (Eds.) ‘Information sciences and systems’, (Springer International Publishing, 2013), pp. 199208.
    37. 37)
      • 19. 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. 063020063020.
    38. 38)
      • 31. Zhanga, T., Li, X., Tao, D., et al: ‘Multimodal biometrics using geometry preserving projections’, Pattern Recognit., 2008, 41, (3), pp. 805813.
    39. 39)
      • 7. Gökberk, B., Okan ˙irfanoğlu, M., Akarun, L., et al: ‘Learning the best subset of local features for face recognition, pattern recognition’, 2007, 40, (5), pp. 15201532.
    40. 40)
      • 37. Pujol, P., Macho, D., Nadeu, C.: ‘On real-time mean-and- variance normalization of speech recognition features’. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP2006), Toulouse, France, 2006, pp. 773776.
    41. 41)
      • 11. Lam, L., Suen, C.Y.: ‘Application of majority voting to pattern recognition: an analysis of its behavior and performance’, IEEE Trans. Syst. Man Cybern. A, Syst. Humans, 1997, 27, (5), pp. 553568.
    42. 42)
      • 3. Nandakumar, K., Chen, Y., Dass, S.C., et al: ‘Likelihood ratio-based biometric score fusion’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (2), pp. 342347.
    43. 43)
      • 2. Proenca, H.P.: ‘Towards non-cooperative biometric iris recognition’. PhD thesis, submitted to Department of Computer Science, University of Beira Interior, 2006.
    44. 44)
      • 28. UBIRIS Iris Database. Available at from http://iris.di.ubi.pt, accessed 2009.
    45. 45)
      • 39. Chen, D., Cao, X., Wen, F., et al: ‘Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification’. Computer Vision and Pattern Recognition (CVPR), 2013.
    46. 46)
      • 10. Nandakumar, K.: ‘Integration of multiple cues in biometric systems’. Master of Science thesis, Michigan State University, 2005.
    47. 47)
      • 27. CASIA-IrisV3. Available at http://www.cbsr.ia.ac.cn/IrisDatabase.htm, accessed 2009.
    48. 48)
      • 17. Vasta, M., Singh, R., Noore, A.: ‘Integrating image quality in 2v-SVM biometric match score fusion’, Int. J. Neural Syst., 2007, 17, (5), pp. 343351.
    49. 49)
      • 50. Tao, Q., Raymond, V.: ‘Robust biometric score fusion by naive likelihood ratio via receiver operating characteristics’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (2), pp. 305313.
    50. 50)
      • 22. Eskandari, M., Toygar, Ö.: ‘Selection of optimized features and weights on face–iris fusion using distance images’, Comput. Vis. Image Underst., 2015, 137, pp. 6375.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0060
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0060
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading