Eye Gaze Detection Based on Learning Automata by Using SURF Descriptor
محورهای موضوعی : Image Processing
Hassan Farsi
1
(University of Birjand)
Reza Nasiripour
2
(University of Birjand)
Sajad Mohammadzadeh
3
(University of Birjand)
کلید واژه: Iris retrieval , SURF , Learning automata , Feature extraction , Classification , Biometrics,
چکیده مقاله :
In the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person identification. The aim of this paper is to achieve higher recognition rate compared to learning automata based methods. Usually, iris retrieval based systems consist of several parts as follows: pre-processing, iris detection, normalization, feature extraction and classification which are captured from eye region. In this paper, a new method without normalization step is proposed. Meanwhile, Speeded up Robust Features (SURF) descriptor is used to extract features of iris images. The descriptor of each iris image creates a vector with 64 dimensions. For classification step, learning automata classifier is applied. The proposed method is tested on three known iris databases; UBIRIS, MMU and UPOL database. The proposed method results in recognition rate of 100% for UBIRIS and UPOL databases and 99.86% for MMU iris database. Also, EER rate of the proposed method for UBIRIS, UPOL and MMU iris database are 0.00%, 0.00% and 0.008%, respectively. Experimental results show that the proposed learning automata classifier results in minimum classification error, and improves precision and computation time.
In the last decade, eye gaze detection system is one of the most important areas in image processing and computer vision. The performance of eye gaze detection system depends on iris detection and recognition (IR). Iris recognition is very important role for person identification. The aim of this paper is to achieve higher recognition rate compared to learning automata based methods. Usually, iris retrieval based systems consist of several parts as follows: pre-processing, iris detection, normalization, feature extraction and classification which are captured from eye region. In this paper, a new method without normalization step is proposed. Meanwhile, Speeded up Robust Features (SURF) descriptor is used to extract features of iris images. The descriptor of each iris image creates a vector with 64 dimensions. For classification step, learning automata classifier is applied. The proposed method is tested on three known iris databases; UBIRIS, MMU and UPOL database. The proposed method results in recognition rate of 100% for UBIRIS and UPOL databases and 99.86% for MMU iris database. Also, EER rate of the proposed method for UBIRIS, UPOL and MMU iris database are 0.00%, 0.00% and 0.008%, respectively. Experimental results show that the proposed learning automata classifier results in minimum classification error, and improves precision and computation time.
[1] Z. Zhu, Q. Ji, "Robust real-time eye detection and tracking under variable lighting conditions and various face orientations", In Computer Vision and Image Understanding, Vol. 98, Vo. 1, 2005, pp. 124-154.
[2] L. Ma, Y. Wang, T. Tan., "Iris recognition based on multichannel Gabor filtering", Proc. Fifth Asian Conf. Computer Vision, 2002, pp. 279-283.
[3] W. Boles, W., B. Boashash, "A human identification technique using images of the iris and wavelet transform", IEEE transactions on signal processing, Vol. 46, No. 4, 1998, pp. 1185-1188.
[4] A. Sović, D. Seršić., "Robustly adaptive wavelet filter bank using L1 norm", in Systems, Signals and Image Processing (IWSSIP), 18th International Conference on. IEEE, 2011.
[5] Y. Zhu, T. Tan, Y. Wang, "Biometric personal identification based on iris patterns. in Pattern Recognition", Proceedings. 15th International Conference on. IEEE, 2000.
[6] T. Bulow, G. Sommer, "Hypercomplex signals-a novel extension of the analytic signal to the multidimensional case", IEEE Transactions on signal processing, Vol. 49, No. 11, 2001, pp. 2844-2852. [7] D. Monro, S. Rakshit, D. Zhang, "DCT-based iris recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, , No. 4, 2007. [8] T. Pradeepthi, A.P. Ramesh. "Pipelined architecture of 2D-DCT, Quantization and Zigzag process for JPEG image compression using VHDL", International Journal of VLSI Design & Communication Systems, Vol. 2, No. 3, 2011.
[9] R. Ng, Y.H. Tay, K.M. Mok, "An effective segmentation method for iris recognition system", IET image processing, 2008.
[10] C. Belcher, Y. Du, "Region-based SIFT approach to iris recognition", Optics and Lasers in Engineering, Vol. 47, 2009, No. 1, 2009, pp. 139-147.
[11] L. Liam, A. Chekima, LC. Fan, J.A, Dargham, "Iris recognition using self-organizing neural network", in Research and Development, 2002. [12] M. Moinuddin, M. Deriche, S.S.A. Ali, "A New Iris Recognition Method based on Neural Networks", WSEAS Transactions on information science and applications, 2004.
[13] H. Ali, M.J. Salami, "Iris recognition system using support vector machines", in Biometric Systems, Design and Applications. InTech, 2004.
[14] W. Zhang, S. Shan, L. Qing, X. Chan, W. Gao, "Are Gabor phases really useless for face recognition?", Pattern Analysis and Applications, Vol. 12, No. 3, 2009, pp. 301-307.
[15] A.M Sarhan, "Iris Recognition Using Discrete Cosine Transform", Journal of Computer Science, Vol. 5,No. 5, 2009, pp. 369-373. [16] P.F.G Mary, P.S.K. Paul, J. Dheeba, "Human identification using periocular biometrics", International Journal of Science, Engineering and Technology Research (IJSETR) , 2013.
[17] R.H Abiyev, K. Altunkaya., "Personal iris recognition using neural network", International Journal of Security and its Applications, Vol. 2, No. 2, 2008, pp. 41-50.
[18] P.C Murty, E.S. Reddy, "Iris recognition system using principal components of texture characteristics", TECHNIA-Int. J. Computing Science and Communication Technologies, Vol. 2, No. 1, 2009, pp. 343-348.
[19] A. Ross, M.S. Sunder, "Block based texture analysis for iris classification and matching", in Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference on, 2010.
[20] R. Farouk, R. Kumar, K. Riad., "Iris matching using multi-dimensional artificial neural network", IET Computer Vision, Vol. 5,No. 3, 2011, pp. 178-184.
[21] A. Varshney, A. Rani, V. Singh., "Optimization of filter parameters for iris detection", in Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 2015.
[22] K. Hajari, U. Gawande, Y. Golhar., "Neural Network Approach to Iris Recognition in Noisy Environment", Procedia Computer Science, 2016, pp. 675-682.
[23] S.H Zahiri, "Learning automata based classifier", Pattern Recognition Letters, Vol. 29, 2008, No. 1, 2008, pp. 40-48.
[24] http://iris.di.ubi.pt/ubiris1.html.
[25] http://www.cbsr.ia.ac.cn:8080/iapr_database.jsp.
[26] http://pesona.mmu.edu.my/.
[27] H. Farsi, R. Nasiripour, S. Mohamadzadeh., "Improved Generic Object Retrieval In Large Scale Database By SURF Descriptor", Journal of Information Systems and Telecommunication (JIST). Vol. 5, No. 2, 2017, pp. 128-137.
[28] H. Bay, A. Ess, T. Tuytelaars, L.V. Gool, "Speeded-up robust features (SURF)", Computer vision and image understanding, Vol. 110, No. 3, 2008, pp. 346-359.
[29] M. obaidat, G. Papadimitiou, A. Pomportsis, "Guest Editorial Learning Automata: Theory, Paradigms and Applications", IEEE Transactions on systems, man, and cybernetics, Vol. 32. No. 6, 2002.
[30] Thathachar, M.A. and P.S. Sastry., "Varieties of learning automata: an overview", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 32, No. 6, pp. 711-722, 2002.
[31] K.S Narendra, M.A. Thathachar., "Learning automata: an introduction", Courier Corporation, 2012.
[32] D.D Zhang, "Automated biometrics", Technologies and systems. Vol. 7.: Springer Science & Business Media, 2007.
[33] M. Elgamal, N. Al-Biqami, "An efficient feature extraction method for iris recognition based on wavelet transformation", Int. J. Comput. Inf. Technol, Vol. 2, No. 03, 2013, pp. 521-527.
[34] A. Kumar, A. Passi, "Comparison and combination of iris matchers for reliable personal authentication", Pattern recognition. Vol. 43, No. 3, 2013, pp. 1016-1026.
[35] A.D. Rahulkar, R.S. Holambe, "Half-iris feature extraction and recognition using a new class of biorthogonal triplet half-band filter bank and flexible k-out-of-n: a postclassifier", IEEE Transactions on Information Forensics and Security, Vol. 7, No.1, 2012, pp. 230-240.
[36] M. Baqar, A. Azhar, Z. Lqbal and et al., "Efficient iris recognition system based on dual boundary detection using robust variable learning rate Multilayer Feed Forward neural network", in Information Assurance and Security (IAS), 7th International Conference on. 2011.
[37] R.M Sundaram, B.C. Dhara, "Neural network based Iris recognition system using Haralick features. in Electronics Computer Technology (ICECT), 2011.
[38] V. Tallapragada, E. Rajan, "Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden Markov model", IET image processing, Vol. 6, No. 6, 2012, pp. 661-667.
[39] C.C Tsai, et al., "Iris recognition using possibilistic fuzzy matching on local features", IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), Vol. 42, No. 1, , 2012, pp. 150-162.
[40] N.N Babu, V. Vaidehi, "Fuzzy based IRIS recognition system (FIRS) for person identification", in Recent Trends in Information Technology (ICRTIT), International Conference on. 2011.
[41] 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, No. 24, 2009, pp. 126-129.
[42] K. Masood, M.Y. Javed, A. Basit., "Iris recognition using wavelet", in Emerging Technologies, ICET. International Conference on. 2007.
[43] S. Umer, B.C. Dhara, B. Chanda., "Iris recognition using multiscale morphologic features", Pattern Recognition Letters, 2015, pp. 67-74.
[44] G. Sachdeva, B. Kaur, "Iris Recognition Using Fuzzy SVM Based On SIFT Feature Extraction Method", in International Journal of Modern Computer Sience (IJMCS), Vol. 4, No. 2, 2016, pp. 16-22.
[45] S. Salve, S. Narote, "Iris recognition using SVM and ANN", in Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on. 2016. IEEE.
[46] N. Liu, H. Li, M. Zhang, J. Liu, Z. Sun, T. Tan, "Accurate iris segmentation in non-cooperative environments using fully convolutional networks", In Proceedings of the IEEE International Conference on Biometrics, Halmstad, Sweden, 2016, pp. 1–8.
[47] M. Arsalan, H. Gil, R. Naqvi, M. Lee, M. Kim, D. Kim, C. Sik, K. Park, "Deep Learning-Based Ireis Segmentation for iris recognition in visible light environment", in MDPI Journal, 2017.
[48] M. Alhamrouni, "Iris Recognition By Using Image Processing Techniques", A thesis submitted to the Graduate School of Natural And Applied Science of ATLIM University, 2017.
[49] B. Shekar, S. Sharada, "Multi-patches iris based person authentication system using particle swarm optimization and fuzzy c-means clustering", in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017.
