Deep Learning Based Detection and Recognition of IRIS Using Convolution Neural Network
Keywords:
Iris, CNN, FCM, GLCMAbstract
The paper proposes a hybrid approach for iris detection and recognition using Fuzzy C Means (FCM), Gray-Level Co-occurrence Matrix (GLCM), and Convolutional Neural Networks (CNN) in MATLAB 2013a version. The proposed method consists of several steps, including pre-processing, segmentation using FCM, feature extraction using GLCM, and classification using a CNN model. The segmentation using FCM and feature extraction using GLCM enable the extraction of more discriminative features for better classification performance. The CNN model is trained on the extracted features and achieves high accuracy in iris recognition. The proposed method outperforms existing methods in terms of both accuracy and computational efficiency. The approach presented in this paper is promising and could be applied in various applications such as security systems, access control, and healthcare.
References
- Liam, L., Chekima, A., Fan, L., Dargham, J.: Iris recognition using self-organizing neural network. In: IEEE, 2002 Student Conference on Research and Developing Systems, Malaysia, 2002, pp. 169–172 (2002)
- Du, Y., Ives, R., Etter, D., Welch, T., Chang, C.: A new approach to iris pattern recognition. In: SPIE European Symposium on Optics/Photonics in Defence and Security, London, UK, October 2004
- Proença, H., Alexandre, L.A.: Iris segmentation methodology for non-cooperative recognition. In: IEEE Proceedings Vision, Image and Signal Processing, vol. 153, no. 2 (2006)
- Procenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010)
- Theckedath, D.: Iris detection based on principal component analysis-eigen irises. In: SPITIEEE Colloquium and International Conference, vol. 1, pp. 49–52
- Popescu-Bodorin, N., Balas, V.E.: Comparing Haar-hilbert and Log-Gabor based iris encoders on bath iris image database. In: Fourth IEEE International Workshop on Soft Computing Application, pp. 191–196 (2010)
- Shashi Kumar, D.R., et al.: PCA based iris recognition using DWT. Int. J. Comp. Tech. Appl. 2(4), 884–893
- Mastronardi, G., Cariello, L., Daleno, D., Castellano, M.: Geodesic distances and hidden markov models for the 3D face recognition markov models for the 3D face recognition, Via Pavoncelli, 139–70125 Bari–Italy
- Li J., Najami, A., Gray, R.M. Image classification by a two dimensional hidden markov model. Information systems laboratory, EE Dept. stanford university, CA 94305.
- Nwe, N.M.M., Sein, M.M.: Classify the high-resolution satellite images. Department of Information Technology, Ph.D. Candidate Mandalay Technological University
- Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. In: Readings in Speech Recognition, pp. 267–296, Morgan Kaufmann, San Francisco, Calif, USA (1990)
- Mattar, E.: Principal components analysis based iris recognition and identification system. Int. J. Soft Comput. Eng. (IJSCE) 3(2) (2013). ISSN: 2231-2307
- Viriri, S., Tapamo, J.-R.: Improving iris-based personal identification using maximum rectangular region detection. School of Computer Science University of KwaZulu-Natal, Durban, South Africa
- Nabti, M., Ghouti, L., Bouridane, A.: An effective and fast iris recognition system based on a combined multiscale feature extraction technique. King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia, Received 1 May 2007, accepted 26 June 2007
- Daugman, J.: How iris recognition works. In: Proceedings of 2002 International Conference on Image Processing, vol. 1 (2002)
- Ghosh, S., Dubey, S.K.: Comparative analysis of k-means and fuzzy c-means algorithms. Department of Computer Science and Engineering, Amity University, Uttar Pradesh Noida, India
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