Multimodal Priority Verification of Face and Speech Using Momentum Back-Propagation Neural Network

  • Changhan Park
  • Myungseok Ki
  • Jaechan Namkung
  • Joonki Paik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


In this paper, we propose a priority verification method for multimodal biometric features by using a momentum back-propagation artificial neural network (MBP-ANN). We also propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses MBP-ANN for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which proves that the proposed method overcomes the limitation of single biometric system and proves stable personal verification in real-time.


Hide Markov Model Face Recognition Speech Recognition Face Image Verification Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Kong, S., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent Advances in Visual and Infrared Face Recognition - A review. Computer Vision, Image Understanding 97(1), 103–135 (2005)CrossRefGoogle Scholar
  2. Kriegman, D., Yang, M., Ahuja, N.: Detecting Faces in Images: a Survey. IEEE Trans. Pattern Analysis, Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  3. Liu, X., Chen, T., Kumar, V.: On Modeling Variations for Face Authentication. In: Proc. 2002 Int. Conf. Automatic Face, Gesture Recognition, pp. 369–374 (2002)Google Scholar
  4. Gu, Y., Thomas, T.: A Hybrid Score Measurement for HMM-based Speaker Verification. In: Proc. 1999 IEEE Int. Conf. Acoustics, Speech, Signal Processing, vol. 1, pp. 317–320 (1999)Google Scholar
  5. Fu, R., Xu, T., Pan, Z.: Modeling of the Adsorption of Bovine Serum Albumin on Porous Polyethylene Membrane by Back-propagation Artificial Neural Network. Journal, Membrane Science 251, 137–144 (2004)CrossRefGoogle Scholar
  6. Devillers, J.: Neural Network in QSAR and Drug Design. Academic Press, San Diego (1996)Google Scholar
  7. Rowley, H., Baluja, S., Kanade, T.: Neural Network-based Face Detection. IEEE Trans. Pattern Analysis, Machine Intelligence 20(1), 203–208 (1998)Google Scholar
  8. Samaria, F., Young, S.: HMM Based Architecture for Face Identification. Image, Vision Computing 12(8), 537–543 (1994)CrossRefGoogle Scholar
  9. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs Fisherfaces: Recognition Using Class Specification Linear Projection. IEEE Trans. Pattern Analysis, Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  10. Kim, Y., Park, C., Paik, J.: A New 3D Active Camera System for Robust Face Recognition by Correcting Pose Variation. In: Proc. 2004 Int. Conf. Circuits, Systems, pp. 1482–1487 (2004)Google Scholar
  11. Zhang, J., Yan, Y., Lades, M.: Face Recognition: Eigenface, Elastic Matching, and Neural Nets. Proc. IEEE 85(9), 1423–1435 (1997)CrossRefGoogle Scholar
  12. Sirivich, L., Kirby, M.: Low-dimensional Procedure for the Characterization of Human Faces. Journal, Optical Society of America A: Optics, Image Science, Vision 4(3), 519–524 (1987)CrossRefGoogle Scholar
  13. Rabiner, L., Juang, B.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Changhan Park
    • 1
  • Myungseok Ki
    • 2
  • Jaechan Namkung
    • 3
  • Joonki Paik
    • 1
  1. 1.Image Processing and Intelligent Systems Laboratory, Department of Image Engineering, Graduate School of Advanced Imaging Science, Multimedia, and FilmChung-Ang UniversitySeoulKorea
  2. 2.Broadcasting Media Research Group, Digital Broadcasting Research Division, ETRIDaejeonKorea
  3. 3.Intelligent Image Communication Laboratory, Department of Computer EngineeringKwangwoon UniversitySeoulKorea

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