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Convolutional Neural Network Based Feature Extraction for IRIS Recognition


Affiliations
1 Computer Science Department, King Abdulaziz University, Jeddah, Saudi Arabia
 

Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a high degree of assurance. Extracting good features is the most significant step in the iris recognition system. In the past, different features have been used to implement iris recognition system. Most of them are depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed system is investigated when extracting features from the segmented iris image and from the normalized iris image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIA-Iris- V1, CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the very high accuracy rate.

Keywords

Biometrics, Iris, Recognition, Deep Learning, Convolutional Neural Network (CNN), Feature Extraction (FE).
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  • M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification," Expert Systems with Applications, vol. 42, pp. 7905-7916, 2015.

  • D. Kesavaraja, D. Sasireka, and D. Jeyabharathi, "Cloud software as a service with iris authentication," Journal of Global Research in Computer Science, vol. 1, pp. 16-22, 2010.

  • N. Shah and P. Shrinath, "Iris Recognition System–A Review," International Journal of Computer and Information Technology, vol. 3, 2014.

  • A. B. Dehkordi and S. A. Abu-Bakar, "A review of iris recognition system," Jurnal Teknologi, vol. 77, 2015.

  • S. Minaee, A. Abdolrashidiy, and Y. Wang, "An experimental study of deep convolutional features for iris recognition," in Signal Processing in Medicine and Biology Symposium (SPMB), 2016 IEEE, 2016, pp. 1-6.

  • S. Minaee, A. Abdolrashidi, and Y. Wang, "Iris recognition using scattering transform and textural features," in Signal Processing and Signal Processing Education Workshop (SP/SPE), 2015 IEEE, 2015, pp. 37-42.

  • S. Minaee, A. Abdolrashidi, and Y. Wang, "Face Recognition Using Scattering Convolutional Network," arXiv preprint arXiv:1608.00059, 2016.

  • IIT Delhi Database. Available: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Iris.htm. Accessed 14 April 2017.

  • ( 2 April2017). CASIA Iris Image Database Version 1.0. Available: http://www.idealtest.org/findDownloadDbByMode.do?mode=Iris. Accessed 12 April 2017.

  • CASIA Iris Image Database Version 4.0 (CASIA-Iris-Thousand). Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=4. Accessed 17 April 2017.

  • CASIA Iris Image Database Version 3.0 (CASIA-Iris-Interval). Available: http://biometrics.idealtest.org/dbDetailForUser.do?id=3. Accessed 17 April2017.

  • K. Nguyen, C. Fookes, A. Ross, and S. Sridharan, "Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective," IEEE Access, 2017.

  • A. Romero, C. Gatta, and G. Camps-Valls, "Unsupervised deep feature extraction for remote sensing image classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 1349-1362, 2016.

  • O. Oyedotun and A. Khashman, "Iris nevus diagnosis: convolutional neural network and deep belief network," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 25, pp. 1106-1115, 2017.

  • A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. Nagem, "A multi-biometric iris recognition system based on a deep learning approach," Pattern Analysis and Applications, pp. 1-20, 2017.

  • J. Nagi, F. Ducatelle, G. A. Di Caro, D. Cireşan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, and L. M. Gambardella, "Max-pooling convolutional neural networks for vision-based hand gesture recognition," in Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on, 2011, pp. 342-347.

  • D. Scherer, A. Müller, and S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," Artificial Neural Networks–ICANN 2010, pp. 92-101, 2010.

  • J. van Doorn, "Analysis of deep convolutional neural network architectures," 2014.

  • C. L. Lam and M. Eizenman, "Convolutional neural networks for eye detection in remote gaze estimation systems," 2008.

  • S. Ahmad Radzi, K.-H. Mohamad, S. S. Liew, and R. Bakhteri, "Convolutional neural network for face recognition with pose and illumination variation," International Journal of Engineering and Technology (IJET), vol. 6, pp. 44-57, 2014.

  • K. Itqan, A. Syafeeza, F. Gong, N. Mustafa, Y. Wong, and M. Ibrahim, "User identification system based on finger-vein patterns using Convolutional Neural Network," ARPN Journal of Engineering and Applied Sciences, vol. 11, pp. 3316-3319, 2016.

  • S. Sangwan and R. Rani, "A Review on: Iris Recognition."

  • C. Jayachandra and H. V. Reddy, "Iris Recognition based on Pupil using Canny edge detection and KMeans Algorithm," Int. J. Eng. Comput. Sci., vol. 2, pp. 221-225, 2013.

  • L. A. Elrefaei, D. H. Hamid, A. A. Bayazed, S. S. Bushnak, and S. Y. Maasher, "Developing Iris Recognition System for Smartphone Security," Multimedia Tools and Applications, pp. 1-25, 2017.

  • A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.

  • S. Minaee and Y. Wang, "Palmprint Recognition Using Deep Scattering Convolutional Network," arXiv preprint arXiv:1603.09027, 2016.

  • J. Weston and C. Watkins, "Multi-class support vector machines," Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, May1998.

  • G. Xu, Z. Zhang, and Y. Ma, "A novel method for iris feature extraction based on intersecting cortical model network," Journal of Applied Mathematics and Computing, vol. 26, pp. 341-352, 2008.

  • M. Abhiram, C. Sadhu, K. Manikantan, and S. Ramachandran, "Novel DCT based feature extraction for enhanced iris recognition," in Communication, Information & Computing Technology (ICCICT), 2012 International Conference on, 2012, pp. 1-6.

  • M. Elgamal and N. Al-Biqami, "An efficient feature extraction method for iris recognition based on wavelet transformation," Int. J. Comput. Inf. Technol, vol. 2, pp. 521-527, 2013.

  • B. Bharath, A. Vilas, K. Manikantan, and S. Ramachandran, "Iris recognition using radon transform thresholding based feature extraction with Gradient-based Isolation as a pre-processing technique," in Industrial and Information Systems (ICIIS), 2014 9th International Conference on, 2014, pp. 1-8.

  • S. S. Dhage, S. S. Hegde, K. Manikantan, and S. Ramachandran, "DWT-based feature extraction and radon transform based contrast enhancement for improved iris recognition," Procedia Computer Science, vol. 45, pp. 256-265, 2015.


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  • Convolutional Neural Network Based Feature Extraction for IRIS Recognition

Abstract Views: 253  |  PDF Views: 210

Authors

Maram G. Alaslani
Computer Science Department, King Abdulaziz University, Jeddah, Saudi Arabia
Lamiaa A. Elrefaei
Computer Science Department, King Abdulaziz University, Jeddah, Saudi Arabia

Abstract


Iris is a powerful tool for reliable human identification. It has the potential to identify individuals with a high degree of assurance. Extracting good features is the most significant step in the iris recognition system. In the past, different features have been used to implement iris recognition system. Most of them are depend on hand-crafted features designed by biometrics specialists. Due to the success of deep learning in computer vision problems, the features learned by the Convolutional Neural Network (CNN) have gained much attention to be applied for iris recognition system. In this paper, we evaluate the extracted learned features from a pre-trained Convolutional Neural Network (Alex-Net Model) followed by a multi-class Support Vector Machine (SVM) algorithm to perform classification. The performance of the proposed system is investigated when extracting features from the segmented iris image and from the normalized iris image. The proposed iris recognition system is tested on four public datasets IITD, iris databases CASIA-Iris- V1, CASIA-Iris-thousand and, CASIA-Iris- V3 Interval. The system achieved excellent results with the very high accuracy rate.

Keywords


Biometrics, Iris, Recognition, Deep Learning, Convolutional Neural Network (CNN), Feature Extraction (FE).

References