Cancelable Iris Recognition System with Pre-trained Convolutional Neural Networks

Document Type : Original Article

Authors

1 Department of nuclear safety and radiological emergencies NCRRT, Egyptian Atomic Energy Authority (EAEA) Egypt

2 Mathematics and Computer Science Department Faculty of Science, Menoufia University Egypt

3 Computer Science & Engineering Dept. Faculty of Eletronic Engineering, Menoufia University Egypt

4 Electronics & Communication Dept. Faculty of Eletronic Engineering, Menoufia University Egypt

Abstract

Iris recognition is one of the automated processes of verifying individuals’ identity based on their iris characteristics. Apparently, the random nature of the iris texture, which is unique for each individual, makes it an exclusive trait for biometric recognition even for the case of identical twins’ authentication. Recently, the improvement in deep learning and computer vision indicated that the extracted features using convolutional neural networks (CNNs) are suitable to describe the complex image patterns. But, how to protect the biometric data and provide users’ privacy is a main concern, nowadays. In this paper, we study the performance of pre-trained CNNs to successfully classify cancelable iris features when taking the feature vector from each fully connected layer. We show that these pre-trained CNNs, while originally learned for classifying generic objects, are also extremely good for representing iris images for recognition. The performance metrics are evaluated on three datasets: CASIA-IrisV3, IITD and Palacky iris databases. The obtained results achieve promising cancelable iris recognition and also ensure the robustness and effectiveness of the proposed approach.

Keywords


[1] A. Muron and J. Pospisil, “The human iris structure and its usages,” in Acta Univ Plalcki Physica, vol 39, pp. 87–95, 2000.
 [2] A. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol.  14, no. 1, pp. 4–20, 2004.
[3] J. Daugman, “How iris recognition works?”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, 2004.
[4] J. Daugman and C. Downing, “Searching for doppelgangers: assessing the universality of the iriscode impostors distribution,” IET Biometrics, vol 5, pp. 65–75, 2016.
[5] J. Daugman, “Information theory and the iriscode,” IEEE Transactions on Information Forensics and Security, vol. 11, pp. 400–409, 2016.
[6] NIST, “IREX III - performance of iris identification algorithms,” National Institute of Science and Technology, USA, Tech. Rep. NIST Interagency Report 7836, 2012.
[7] ——, “IREX IV - evaluation of iris identification algorithms,” National Institute of Science and Technology, USA, Tech. Rep. NIST Interagency Report 7949, 2013.
[8] J. Daugman, “Major international deployments of the iris recognition algorithms: a billion persons,” Report, Dec 2014.
[9] J. Daugman, “New methods in iris recognition,” IEEE Transactions on Systems, Man and Cybernetics, vol. 37, no. 11, pp. 67-75, 2007.
[10] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: A survey,” Computer Vision and Image Understanding, vol. 110, pp. 281-307, 2008.
[11] ——, Handbook of Iris Recognition. UK: Springer-Verlag, 2013, ch. 2. A Survey of Iris Biometrics Research: 2008-2010.
[12] I. Nigam, M. Vatsa, and R. Singh, “Ocular biometrics: A survey of modalities and fusion approaches,” Information Fusion, vol. 26, pp. 1-35, 2015.
[13] K. Nguyen, C. Fookes, R. Jillela, S. Sridharan, and A. Ross, “Long range iris recognition: A survey,” Pattern Recognition, vol. 72, pp. 123-143, 2017.
[14] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature vol. 521, no. 7553, pp. 436-444, 2015.
[15] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, pp. 85-117, 2015.
[16] Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798–1828, 2013.
[17] M. Tarek, O. Ouda, T. Hamza, ”Pre-image resistant cancelable biometrics scheme using bidirectional memory model,” Int. J. of Netw. Sec., vol. 19, no. 4, pp. 498-506, 2017.
[18] N. K. Ratha, S. Chikkerur, J. H. Connell, R. M. Bolle, “Generating Cancelable Fingerprint templates,” IEEE Trans. on Pattern Anal. Mach., vol.  29, no. 4, pp. 561-572, 2007
[19] A. Harjoko, S. Hartati, H. Dwiyasa,”A method for iris recognition based on 1d coiflet wavelet,” World Academy of Science Engineering and technology, vol. 56, pp. 126–129, 2009
[20] A. Ignat, M. Luca, A. Ciobanu,”Iris features using dual tree complex wavelet transform in texture evaluation for biometrical identification,” IEEE E-Health and Bioengineering Conference, pp. 1–4, 2013
[21] J. K. Pillai, V. M. Patel, R. Chellappa, and N. K. Ratha, “Sectored random projections for cancelable iris biometrics,” Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, pp. 1838–1841, Mar. 2010.
[22] J. Zuo, N. Ratha, and J. Connell, “Cancelable iris biometric,” Proc. Int. Conf. Pattern Recognition, pp. 1-4, 2008.
[23] C. Rathgeb, A. Uhl,” Secure iris recognition based on local intensity variations,” Proc. of the 7th Int. Conf. on Image Analysis and Recognition, pp. 266-275, 2010.
[24] M. A. Syarif, T. S. Ong, A. B. J. Teoh, C. Tee,” Improved Biohashing Method Based on Most Intensive Histogram Block Location,” Int. Conf. of Neural Information Processing,  pp. 644-652, 2014.
[25] A. B. J. Teoh, D. C. L. Ngo, A. Goh,” Biohashing: two factor authentication featuring fingerprint data and tokenised random number,” Pattern Recog., vol. 37, no. 11, pp. 2245-2255, 2004.
[26] C. Rathgeb, F. Breitinger, H. Baier, C. Busch,” Towards bloom filter-based indexing of iris biometric data,” 15th IEEE Int. Conf. on Biometrics, pp. 422-429, 2015.
[27] C. Rathgeb, F. Breitinger, C. Busch, H. Baier, “On the application of bloom filters to iris biometrics,” IET J. On Biometrics, vol.  3, no. 4, pp. 207-218, 2014.
[28] A. Canziani, A. Paszke, and E. Culurciello, “An analysis of deep neural network models for practical applications,” CoRR, vol. abs/1605.07678, 2016.
[29] D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture of monkey striate cortex,” Journal of Physiology (London), vol. 195, pp. 215–243, 1968.
[30] K. Fukushima and S. Miyake, “Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition,” Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 267–285, 1982.
[31] Y. LeCun, B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, L. D. Jackel, “Handwritten digit recognition with a backpropagation network,” Advances in Neural Information Processing Systems, Morgan-Kaufmann, pp. 396–404, 1990.
[32] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, no. 2, pp. 1097-1105, 2012.
[33] A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn features off-the-shelf: An astounding baseline for recognition,” IEEE Conf. on Computer Vision and Pattern Recognition Workshops, pp. 512–519, Jun 2014.
[34] N. Liu, M. Zhang, H. Li, Z. Sun, and T. Tan, “Deepiris: Learning pairwise filter bank for heterogeneous iris verification,” Pattern Recognition Letters, vol. 82, pp. 154-161, 2016.
[35] P. A. Johnson, P. Lopez-Meyer, N. Sazonova, F. Hua, S. Schuckers, “Quality in face and iris research ensemble (q-fire),” Fourth IEEE Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6, Sep 2010.
[36] Chinese Academy of Sciences Institute of Automation, “CASIA iris image database, http://biometrics.idealtest.org/,” Aug 2017.
[37] A. Gangwar and A. Joshi, “Deepirisnet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition,” IEEE Int. Conf. on Image Processing (ICIP), pp. 2301–2305, Sep. 2016.
[38] P. J. Phillips, W. T. Scruggs, A. J. O’Toole, P. J. Flynn, K. W. Bowyer, C. L. Schott, M. Sharpe, “Frvt 2006 and ice 2006 largescale experimental results,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.  32, no. 5, pp. 831–846, 2010.
[39] D. Menotti, G. Chiachia, A. Pinto, W. R. Schwartz, H. Pedrini, A. X. Falco, and A. Rocha, “Deep representations for iris, face, and fingerprint spoofing detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 864–879, 2015.
[40] N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, and T. Tan, “Accurate iris segmentation in non-cooperative environments using fully convolutional networks,” Int. Conf. on Biometrics (ICB), pp. 1–8, Jun 2016.
[41] J. Tapia and C. Aravena, “Gender Classification from NIR Iris Images Using Deep Learning,” Cham: Springer International Publishing, pp. 219–239, 2017.
[42] S. Minaee, A. Abdolrashidiy, and Y. Wang, “An experimental study of deep convolutional features for iris recognition,” IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1-6, Dec. 2016.
[43] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” CoRR, vol. abs/1409.1556, 2014.
[44] M. Essam, M. Abd Elnaby, Magdi Fikri and F. E. Abd El-Samie, “A Fast Accurate Algorithm for Iris Localization Using a Coarse-to-Fine Approach, ” Japan-Egypt Conf. on Electronics, Communications and Computers, pp.75-79, 2012.
[45] R. Ng, Y. Tay, and K. Mok, “A review of iris recognition algorithms,” Int. Symposium on Information Technology ITSim 2008, vol. 2, pp. 1–7, Aug. 2008.
[46] S. Sanderson, J. Erbetta,” Authentication for secure environments based on iris scanning technology,” IEE Colloquium on Visual Biometrics, 2000.
[47] L. Masek, “Recognition of human iris patterns for biometric identification,” MSc. Thesis, The University of Western Australia, 2003.
[48] E. Maiorana, P. Campisi, J. Fierrez, J. Ortega-Garcia, and A. Neri, “Cancelable templates for sequence-based biometrics with application to on-line signature recognition,” IEEE Trans. Syst., Man Cybern. A, vol.  40, no. 3, pp. 525–538, 2010.
[49] B. Scholkopf and A. J. Smola, “Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond,” USA: MIT Press, 2001.
[50] CASIA-IrisV3 Database, http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp.  Accessed May 2018.
[51] Palack Palacky Iris Database, http://phoenix.inf.upol.cz/iris/; 2004.
[52] IITD Iris Database, http://www4.comp.polyu.edu.hk/_csajaykr/IITD/Database Iris.htm. Accessed May 2018.
 
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 95-101