Bimodal Biometric Person Identification System Under Perturbations

  • Miguel Carrasco
  • Luis Pizarro
  • Domingo Mery
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


Multibiometric person identification systems play a crucial role in environments where security must be ensured. However, building such systems must jointly encompass a good compromise between computational costs and overall performance. These systems must also be robust against inherent or potential noise on the data-acquisition machinery. In this respect, we proposed a bimodal identification system that combines two inexpensive and widely accepted biometric traits, namely face and voice information. We use a probabilistic fusion scheme at the matching score level, which linearly weights the classification probabilities of each person-class from both face and voice classifiers. The system is tested under two scenarios: a database composed of perturbation-free faces and voices (ideal case), and a database perturbed with variable Gaussian noise, salt-and-pepper noise and occlusions. Moreover, we develop a simple rule to automatically determine the weight parameter between the classifiers via the empirical evidence obtained from the learning stage and the noise level. The fused recognition systems exceeds in all cases the performance of the face and voice classifiers alone.


Biometrics multimodal identificacion face voice probabilistic fusion Gaussian noise salt-and-pepper noise occlusions 


  1. 1.
    Prabhakar, S., Pankati, S., Jain, A.K.: Biometric recognition: Security and privacy concerns. IEEE Security and Privacy 01(2), 33–42 (2003)CrossRefGoogle Scholar
  2. 2.
    Jain, A.K.: Biometric recognition: How do i know who you are? In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 19–26. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Ross, A., Jain, A.: Multimodal biometrics: An overview. In: Proc. 12th European Signal Processing Conference, EUSIPCO 2004, Vienna, Austria, pp. 1221–1224 (September 2005)Google Scholar
  4. 4.
    Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recognition Letters 24(13), 2115–2125 (2003)CrossRefGoogle Scholar
  5. 5.
    Brunelli, R., Falavigna, D.: Person identification using multiple cues. IEEE Trans Pattern Anal Mach Intell 17(10), 955–966 (1995)CrossRefGoogle Scholar
  6. 6.
    Bigün, E., Bigün, J., Duc, B., Fischer, S.: Expert conciliation for multi modal person authentication systems by bayesian statistics. In: Bigün, J., Borgefors, G., Chollet, G. (eds.) AVBPA 1997. LNCS, vol. 1206, pp. 291–300. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  7. 7.
    Snelick, R., Uludag, U., Mink, A., Indovina, M., Jain, A.: Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems. IEEE Trans Pattern Anal Mach Intell 27(3), 450–455 (2005)CrossRefGoogle Scholar
  8. 8.
    Jain, A.K., Ross, A.: Multibiometric systems.  47(1), 34–40 (2004)Google Scholar
  9. 9.
    Sanderson, C., Paliwal, K.K.: Identity verification using speech and face information. Digit Signal Process 14(5), 449–480 (2004)CrossRefGoogle Scholar
  10. 10.
    Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: A survey. IEEE Trans Pattern Anal Mach Intell 24(1), 34–58 (2002)CrossRefGoogle Scholar
  11. 11.
    Lu, X.: Image analysis for face recognition: A brief survey. Personal Notes  (May 2003)Google Scholar
  12. 12.
    Ruiz-del-Solar, J., Navarrete, P.: Eigenspace-based face recognition: a comparative study of different approaches. IEEE Trans Syst Man Cybern C Appl Rev 35(3), 315–325 (2005)CrossRefGoogle Scholar
  13. 13.
    Guerfi, S., Gambotto, J.P., Lelandais, S.: Implementation of the watershed method in the hsi color space for the face extraction. In: IEEE Conference on Advanced Video and Signal Based Surveillance, 2005. AVSS 2005, pp. 282–286. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  14. 14.
    Lu, X., Jain, A.: Deformation analysis for 3d face matching. In: Proc. Seventh IEEE Workshops on Application of Computer Vision, WACV/MOTION 2005, pp. 99–104. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  15. 15.
    Doddington, G.R.: Speaker recognition identifying people by their voices. Proc. IEEE 73(11), 1651–1664 (1985)CrossRefGoogle Scholar
  16. 16.
    Furui, S.: Cepstral analysis technique for automatic speaker verification. IEEE Trans Acoust Speech Signal Process 29(2), 254–272 (1981)CrossRefGoogle Scholar
  17. 17.
    Murty, K.S.R., Yegnanarayana, B.: Combining evidence from residual phase and mfcc features for speaker recognition. IEEE Signal Process Lett 13(1), 52–55 (2006)CrossRefGoogle Scholar
  18. 18.
    Picone, J.W.: Signal modeling techniques in speech recognition. Proc. IEEE 81(9), 1215–1247 (1993)CrossRefGoogle Scholar
  19. 19.
    Kirby, M., Sirovich, L.: Application of the karhunen-loeve procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1), 103–108 (1990)CrossRefGoogle Scholar
  20. 20.
    Wei, H., Cheong-Fat, C., Chiu-Sing, C., Kong-Pang, P.: An efficient mfcc extraction method in speech recognition. In: Proc. 2006 IEEE International Symposium on Circuits and Systems, ISCAS 2006, may 2006, pp. 145–148. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  21. 21.
    Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans Comm 28(1), 84–95 (1980)CrossRefGoogle Scholar
  22. 22.
    Kinnunen, I., Kärkkäinen, T.: Class-discriminative weighted distortion measure for vq-based speaker identification. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 681–688. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  23. 23.
    Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proc. 2nd IEEE Workshop on Applications of Computer Vision, pp. 138–142. IEEE Computer Society Press, Los Alamitos (1994)CrossRefGoogle Scholar
  24. 24.
    Dana, K.J., Van-Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real world surfaces. ACM Transactions on Graphics (TOG) 18(1), 1–34 (1999)CrossRefGoogle Scholar
  25. 25.
    Yamauchi, J., Shimamura, T.: Noise estimation using high frequency regions for speech enhancement in low snr environments. In: Proc. of the 2002 IEEE Workshop on Speech Coding, pp. 59–61. IEEE Computer Society Press, Los Alamitos (2002)CrossRefGoogle Scholar
  26. 26.
    Reju, V.G., Tong, Y.C.: A computationally efficient noise estimation algorithm for speech enhancement. In: Proc. of the 2004 IEEE Asia-Pacific Conference on Circuits and Systems, vol. 1, pp. 193–196. IEEE Computer Society Press, Los Alamitos (2004)CrossRefGoogle Scholar
  27. 27.
    Wu, G.D.: A novel background noise estimation in adverse environments. In: Proc. of the 2005 IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1843–1847. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  28. 28.
    Starck, J.L., Murtagh, F.: Automatic noise estimation from the multiresolution support. Publications of the Astronomical Society of the Pacific 110(744), 193–199 (1998)CrossRefGoogle Scholar
  29. 29.
    Salmeri, M., Mencattini, A., Ricci, E., Salsano, A.: Noise estimation in digital images using fuzzy processing. In: Proc. of the 2001 International Conference on Image Processing, vol. 1, pp. 517–520 (2001)Google Scholar
  30. 30.
    Shin, D.H., Park, R.H., Yang, S., Jung, J.H.: Block-based noise estimation using adaptive gaussian filtering. IEEE Transactions on Consumer Electronics 51(1), 218–226 (2005)CrossRefGoogle Scholar
  31. 31.
    Liu, C., Freeman, W.T., Szeliski, R., Kang, S.B.: Noise estimation from a single image. In: Proc. of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 901–908. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  32. 32.
    Grammalidis, N., Strintzis, M.: Disparity and occlusion estimation in multiocular systems and theircoding for the communication of multiview image sequences. IEEE Transactions on Circuits and Systems for Video Technology 8(3), 328–344 (1998)CrossRefGoogle Scholar
  33. 33.
    Ince, S., Konrad, J.: Geometry-based estimation of occlusions from video frame pairs. In: Proc. of the 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 933–936. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Miguel Carrasco
    • 1
  • Luis Pizarro
    • 2
  • Domingo Mery
    • 1
  1. 1.Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860(143), SantiagoChile
  2. 2.Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Bldg. E11, 66041 SaarbrückenGermany

Personalised recommendations