Cancelable Speaker Recognition System based on Chaotic Encryption Approach | ||||
Menoufia Journal of Electronic Engineering Research | ||||
Article 37, Volume 28, ICEEM2019-Special Issue, 2019, Page 83-88 | ||||
Document Type: Original Article | ||||
DOI: 10.21608/mjeer.2019.76772 | ||||
View on SCiNiTO | ||||
Authors | ||||
Neven Hassan1; Walid El-Shafai 2; Naglaa Soliman3; Adel EL-Fishawy4; Fathi E. Abd El-Samie4 | ||||
1Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
2Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
3Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
4Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University: Menouf, Egypt | ||||
Abstract | ||||
Biometric-based authentication system can provide strong safety guarantee of user identity, but it creates other concerns pertaining to template security. There is an urgent issue of preventing the original templates from being abused, and protecting the users' privacy efficiently. This paper introduces a cancellable speaker identification system based on chaotic encryption process to produce cancelable templates instead of original templates. The resulted transformed version of the voice biometrics is stored in the server instead of the original biometrics. So, the users' privacy can be protected well. In the experimental results, we calculate the EER, FRR, FAR, and AROC values for the proposed work. Also, we estimate the score for genuine and impostor and the ROC curve . | ||||
Keywords | ||||
Cancelable Biometrics; Chaotic maps; voice recognition | ||||
References | ||||
[1] H. Kohad, V. R .Ingle, and M. A. Gaikwad ―An Overview of Speech Encryption techniques‖, International Journal of Engineering Research and Development ISSN, vol. 3, Issue 4 , pp. 29-32, 2012.
[2] L. Muda, M. Begam and I. Elamvazuthi ''Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques'', Journal of Computing, Vol. 2, ISSN 2151-9617, March 2010.
[3] Farsana F J, Dr.K.Gopakumar, Private Key Encryption of Speech Signal Based on Three Dimensional Chaotic Map, International Conference on Communication and Signal Processing, pp. 2197-2201,
April 6-8, 2017.
[4] Y.Saleem,M.Amjad,M.H.Rahman,F.Hayat,T.Izhar,M.Saleem,”Speech Encryption,Implementation of One Time Pad Algorithm in Matlab” Pakistan Journal of Science,Vol.65, pp 114-118,March 2013.
[5] R.Aparna,Dr.PL.Chithra,Role of Windowing Techniques in Speech Signal Processing For Enhanced Signal Cryptography,Advanced Engineering Research amd Applications, Chapter 28,Volume V,pp.
446-458,2017.
[6] A. K. Jain, A. A. Ross, and K. Nandakumar, Introduction to Biometrics (Springer, 2017).
[7] V. M. Patel, N. K. Ratha, and R. Chellappa, “Cancelable biometrics: a review,” IEEE Signal Process. Mag. 32, 54–65 (2015).
[8] H. Kaur and P. Khanna, “Non-invertible biometric encryption to generate cancelable biometric templates,” in Proceedings of the World Congress on Engineering and Computer Science, San Francisco, California, 2017, Vol. I, pp. 1–4.
[9] Al Saad, S. N., & Eman, H. (2014) A speech encryption based on chaotic maps. International Journal of Computer Applications, 93(4).
[10] Campbell, J. P. (1997). Speaker recognition: a tutorial. Proceedings of the IEEE, 85(9), 1437–1462.
[11] Furui, S. (1996). An overview of speaker recognition technology. The Kluwer International Series in Engineering and Computer Science, 355, 31–55.
[12] Goldburg, B., Sridharan, S., & Dawson, E. (1990). Speech encryption in the transform domain. Electronics Letters, 26(10), 655–657.
[13] Goldburg, B., Sridharan, S., & Dawson, E. (1993) Design and cryptanalysis of transform-based analog speech scramblers. IEEE on Select Areas on Communications, 11(5), 735–744.
[14] Stallings, W. (2017) Cryptography and network, security: Principles and practice (7th edn.). Upper Saddle River: Prentice-Hall.
[15] Gupta, S., Jaafar, J., Ahmad, W. F. W., & Bansal A. (2013) Feature extraction using MFCC. Signal & Image Processing: An International Journal (SIPIJ), 4(4), 101.
[16] Kohad H., Ingle V. R., & Gaikwad M. A. (2012). An overview of speech encryption techniques, International Journal of Engineering Research and Development, 3(4), 29–32.
[17] Kurzekar, P. K., Deshmukh, R. R., Waghmare, V. B., & Shrishrimal, P. P. (2014). A comparative study of feature extraction techniques for speech recognition system. IJIRSET, 3, 18006–18016.
[18] Manjunath, G., & Anand, G. V. (2002). Speech encryption using circulant transformations. Proceedings IEEE, International Conference Multimedia and Expo, vol. 1, pp. 553–556, August, 2002.
[19] Milton, R. M. A time and frequency-domain speech scrambler, COMSIG 1989 Proceedings, Southern African Conference, pp. 125– 130, June 1989
[20] Muda, L., Begam, M., & Elamvazuthi, I. (2010). Voice recognition algorithms using mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques. Journal of Computing, 2. | ||||
Statistics Article View: 153 |
||||