Evaluation of Hybrid Face and Voice Recognition Systems for Biometric Identification in Areas Requiring High Security

Cheluri Trivikram*, Susanna Samarpitha **, K. Madhavi***, Diana Moses****
*-** UG Scholar, Department of Computer Science and Engineering, St. Peter's Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.
*** Assistant Professor, Department of Computer Science and Engineering, St. Peter's Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.
**** Professor, Department of Computer Science and Engineering, St. Peter's Engineering College, Jawaharlal Nehru Technological University, Hyderabad, India.
Periodicity:September - November'2017
DOI : https://doi.org/10.26634/jpr.4.3.13885

Abstract

Biometric identification is a mandatory tool to secure digital information for various industrial, government, commercial, and security applications. Face recognition is a distinct problem and lacks a unique solution applicable to all situations. Face recognition is not effective in identifying individuals in conditions, when a person is using glasses, hats or has a beard etc. Alternative technologies like Iris and retinal scan biometric techniques need sophisticated equipment, which is not financially viable for all applications. Voice recognition methods have low accuracy and are affected by situations where a change in a person's voice due to illness like cold render absolute identification inaccurate. This paper proposes a biometric method implementing multiple techniques i.e., both face and voice recognition technique as an effective identification tool. Identification process using combined biometric methods makes a foolproof security system, thereby leaving no scope for error. A comprehensive assessment of the performance accuracy of several algorithms like Principal Component Analysis (PCA), Fisher Linear Discriminant Analysis (FLD), Dynamic Time Warping (DTW), Support Virtual Machines (SVM), Neural Networks (NN) implemented in face and voice recognition were studied and then corresponding performance accuracy was reviewed. A new technique has been proposed using these performance accuracy results which would help provide the best hybrid method. The proposed hybrid biometric identification method is a viable solution for industries or areas with need for high security of their data systems.

Keywords

Face Recognition, Voice Recognition, PCA, FLD, DTW, SVM, Neural Networks

How to Cite this Article?

Trivikram, C., Samarpitha, S., Madhavi, K., and Moses, D. (2017). Evaluation of Hybrid Face and Voice Recognition Systems for Biometric Identification in Areas Requiring High Security. i-manager’s Journal on Pattern Recognition, 4(3), 9-16. https://doi.org/10.26634/jpr.4.3.13885

References

[1]. Abiyev, R. H. (2014). Facial feature extraction techniques for face recognition. Journal of Computer Science, 10(12), 2360.
[2]. Al-Allaf, O. N. (2014). Review of face detection systems based artificial neural networks algorithms. The International Journal of Mutimedia & its Applications, 6(1), 1-16.
[3]. Asadi, S., Rao, C. D. S., & Saikrishna, V. (2010). A comparative study of face recognition with principal component analysis and cross-correlation technique. International Journal of Computer Applications, 10(8), 17-21.
[4]. Fang, C. (2009). From dynamic time warping (DTW) to hidden markov model (HMM). University of Cincinnati, 3, 19.
[5]. Ganapathiraju, A., Hamaker, J., & Picone, J. (1998). Support vector machines for speech recognition. In Fifth International Conference on Spoken Language Processing.
[6].Guo, G., Li, S. Z., & Chan, K. (2000). Face recognition by support vector machines. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on (pp. 196-201). IEEE.
[7]. Huang, J., Blanz, V., & Heisele, B. (2002). Face recognition using component-based SVM classification and morphable models. In Pattern Recognition with Support Vector Machines (pp. 334-341). Springer, Berlin, Heidelberg.
[8]. Jain, A. & Harris, H. (2004). Speaker identification using MFCC and HMM based techniques, University of Florida.
[9]. Jha, J. & Ragha, L. (2013). Intrusion detection system using support vector machine. International Journal of Applied Information Systems (HAIS)-ISSN, 2249-0868. 25- 30
[10]. Karamizadeh, S., Abdullah, S. M., Manaf, A. A., Zamani, M., & Hooman, A. (2013). An overview of principal component analysis. Journal of Signal and Information Processing, 4(03), 173-175.
[11]. Kumar, S. & Kaur, H. (2012). Face recognition techniques: Classification and comparisons. International Journal of Information Technology and Knowledge Management, 5(2), 361-363.
[12]. Li, J. (2003). An empirical comparison between SVMs and ANNs for speech recognition. In The First Instructional Conf. on Machine Learning (Vol. 951, p. 2003).
[13]. Madan, A. & Gupta, D. (2014). Speech Feature Extraction and Classification: A Comparative Review. International Journal of computer applications, 90(9).
[14]. Mukkamala, S., Janoski, G., & Sung, A. (2002). Intrusion detection using neural networks and support vector machines. In Neural Networks, 2002. IJCNN'02. Proceedings of the 2002 International Joint Conference on (Vol. 2, pp. 1702-1707). IEEE.
[15]. Navazi, A. S., Dhevisri, T., & Mazumder, P. (2013). Face recognition using principal component analysis and neural networks. March-2013, International Journal of Computer Networking, Wireless and Mobile Communications, (3), 245-256.
[16]. Nugrahaeni, R. N. (2016). Comparative Analysis for Voice Recognition .
[17]. Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K. et al. (2005). Overview of the face recognition grand challenge. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 1, pp. 947-954). IEEE.
[18]. Salvador, S. & Chan, P. (2007). Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561-580.
[19]. Sung, A. H. & Mukkamala, S. (2003). Identifying important features for intrusion detection using support vector machines and neural networks. In Applications and the Internet, 2003. Proceedings. 2003 Symposium on (pp. 209-216). IEEE.
[20]. Thai, L. H., Hai, T. S., & Thuy, N. T. (2012). Image classification using support vector machine and artificial neural network. International Journal of Information Technology and Computer Science (IJITCS), 4(5), 32-38.
[21]. Turk, M. A. & Pentland, A. P. (1991a). Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on (pp. 586-591). IEEE.
[22]. Turk, M. & Pentland, A. (1991b). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71-86.
[23]. Yee, C. S. & Ahmad, A. M. (2008). Malay language text-independent speaker verification using NN-MLP classifier with MFCC. In Electronic Design, 2008. ICED 2008. International Conference on (pp. 1-5). IEEE.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.