Skip to main content

Biometric Fusion System Using Face and Voice Recognition

A Comparison Approach: Biometric Fusion System Using Face and Voice Characteristics

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 996)

Abstract

This paper presents a biometric fusion system for human recognition which utilizes voice and face biometric features. The aim of the system is to verify or identify the users by their face and/or voice characteristics. The system allows reliable human recognition based on physiological biometric feature—face, and physiological–behavioral biometric feature—voice. The outcome yields better identification results using voice biometrics and better verification results using face biometrics. The approach used here for face recognition consists of an image processing being histogram equalization and application of Gabor filter, and extracting a feature vector from the image. Such a feature extraction method has been successfully applied in the iris recognition [3]. The speech recognition process uses a recording divided into frames out of which only the frames containing speech are considered. The feature vector is extracted from Mel frequency cepstral coefficient. The classification process is performed by a basic dynamic time warping algorithm. In this paper, evaluation of the above approaches for face and voice recognition is performed.

Keywords

  • Face recognition
  • Text-dependent voice recognition
  • Biometrics
  • Biometric fusion system
  • Gabor filter
  • Mel frequency cepstral coefficients

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-13-8969-6_5
  • Chapter length: 19 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-981-13-8969-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Bibliography

References

  1. Chakraborty, K., Talele, A., Upadhya, S.: Voice recognition using MFCC algorithm. Int. J. Innov. Res. Adv. Eng. (IJIRAE) 1(10) (2014). ISSN: 2349-2163

    Google Scholar 

  2. Chan, W., Jaitly, N., Le, Q., Vinyals, O.: Listen, attend and spell: a neural network for large vocabulary conversational speech recognition. In: ICASSP (2016)

    Google Scholar 

  3. Choraś, R.: Image feature extraction techniques and their applications for CBIR and biometrics systems. Int. J. Biol. Biomed Eng. 1(1) (2007)

    Google Scholar 

  4. Chung, J.S., Senior, A., Vinyals, O., Zisserman, A.: Lip reading sentences in the wild (2016). arXiv:1611.05358

  5. Furui, S.: Digital Speech Processing Synthesis and Recognition, 2 edn. (2001)

    Google Scholar 

  6. Muda, L., Begam, M., Elamvazuthi, I.: Voice recognition algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) techniques. J. Comput. 2(3). ISSN 2151-9617

    Google Scholar 

  7. Rouhi, R., Amiri, M., Irannejad, B.: A review on feature extraction techniques in face recognition. Sig. Image Process. Int. J. (SIPIJ) 3(6) (2012)

    Google Scholar 

  8. Socolinsky, D.A., Selinger, A.: Thermal face recognition in an operational scenario. In: IEEE Computer Society, pp. 1012–1019 (via ACM Digital Library) (2004)

    Google Scholar 

  9. Starner, T., Pentland, A.: Real-time American sign language visual recognition from video using hidden Markov models. Master’s thesis, MIT Program in Media Arts (1995)

    Google Scholar 

  10. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends Sig. Process. 7(3–4), 197–387 (2014)

    MathSciNet  CrossRef  Google Scholar 

  11. Swaminathan, A.: Face recognition using support vector machines. In: ENEE633: Statistical and Neural Pattern Recognition (2005)

    Google Scholar 

  12. Li, S.Z., Lu, J.: Face recognition using nearest feature line method. IEEE Trans. Neural Netw. 10(2), 439–443 (1999)

    CrossRef  Google Scholar 

  13. Turk, M., Pentland, A.: Face recognition using eigenfaces. In: Computer Vision and Pattern Recognition. Proceedings CVPR’91. IEEE Computer Society Conference (1991)

    Google Scholar 

Internet Sources

  1. FFmpeg official website. https://www.ffmpeg.org/. Accessed 6 Dec 2017

  2. Mel Frequency Cepstral Coefficient (MFCC) tutorial. http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/. Accessed 25 Nov 2017

  3. Minimum Distance Classifiers tutorial. https://homepages.inf.ed.ac.uk/rbf/HIPR2/classify.htm. Accessed 6 Dec 2017

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kornel Żaba .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Kuśmierczyk, A., Sławińska, M., Żaba, K., Saeed, K. (2020). Biometric Fusion System Using Face and Voice Recognition. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-8969-6_5

Download citation