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A Multi-Biometric Iris Recognition System using Convolution Neural Network

Jaya Suma*, Sam Oguri **

* Department of Information Technology, JNTUK-University College of Engineering, Vizianagaram, Andhra Pradesh, India.

** Andhra University, Visakhapatnam, Andhra Pradesh, India.

Periodicity:January - June'2020
DOI : https://doi.org/10.26634/jpr.7.1.17405

Abstract

In many real world applications, multimodal biometric systems are popularly used due to its ability to deal with disadvantages of unimodal biometric systems. In this paper, effective and efficient multimodal biometric system are developed using Convolution Neural Network (CNN) which helps to predict identity of a individual based on his/her iris pattern. The proposed iris recognition systems is a combination of CNN with Soft Max classifier. The performance of the proposed iris is evaluated on IITD database obtained in various conditions. The obtained results shows that the proposed system outperforms the previous approaches. And the four layered CNN with Adadelta optimizer has produced the best results of more than 95% accuracy.

Keywords

Iris Recognition, Multimodal Biometric Systems, Deep Learning, Convolutional Neural Network, Soft Max Classifier, Rank Level.

How to Cite this Article?

Suma, J., and Oguri, S. (2020). A Multi-Biometric Iris Recognition System using Convolution Neural Network. i-manager's Journal on Pattern Recognition, 7(1), 1-7. https://doi.org/10.26634/jpr.7.1.17405

References

[1]. Al Mahafzah, H., & Al Rwashdeh, M. Z. (2012). A Survey of multibiometric systems. International Journal of Computer Applications, 43(15), 36-43.

[2]. Al-Waisy, A. S., Qahwaji, R., Ipson, S., & Al-Fahdawi, S. (2015, October). A robust face recognition system based on curvelet and fractal dimension transforms. In 2015, IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (pp. 548-555). IEEE.

[3]. Das, A., & Parekh, R. (2012). Iris recognition using a scalar based template in Eigen-space. International Journal of Computer Science and Telecommunication, 3(5), 74-79.

[4]. Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148-1161.

[5]. Gad, R., El-Fishawy, N., El-Sayed, A., & Zorkany, M. (2015). Multi-biometric systems: a state of the art survey and research directions. International Journal of Advanced Computer Science and Applications (IJACSA), 6(6), 128-138.

[6]. Hajari, K. (2015). Improving iris recognition performance using local binary pattern and combined RBFNN. International Journal of Engineering and Advanced Technology, 4(4), 108-112.

[7]. Hentati, R., Hentati, M., & Abid, M. (2012). Development of a new algorithm for iris biometric recognition. International Journal of Computer Communication Engineering, 1(3), 283-286.

[8]. Kumari, P. A., & Suma, G. J. (2016, November). An experimental study of feature reduction using PCA in multi-biometric systems based on feature level fusion. In 2016, International Conference on Advances in Electrical, Electronic and Systems Engineering (ICAEES) (pp. 109-114). IEEE.

[9]. Zeng, M., Nguyen, L. T., Yu, B., Mengshoel, O. J., Zhu, J., Wu, P., & Zhang, J. (2014, November). Convolutional neural networks for human activity recognition using mobile sensors. In 6th International Conference on Mobile Computing, Applications and Services (pp. 197-205). IEEE. https://doi.org/10.4108/icst.mobicase.2014.257786

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