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• Daugman, J., “Biometric personal identification system based on iris analysis”, U.S. Patent No. 5,291,560, (1994).
• Daugman, J., “How iris recognition works”, IEEE Transactions on Circuits and Systems for Video Technology, 14(1): 22-30, (2004).
• Daugman, J., “Statistical richness of visual phase information: update on recognizing persons by iris patterns”, International Journal on Computer Vision, 45: 25-38, (2001).
• Daugman, J., “High confidence visual recognition of persons by a test of statistical independence”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11): 1148-61, (1993).
• Alvarez-Betancourt, Y., Garcia-Silvente, M., “A keypoints-based feature extraction method for iris recognition under variable image quality conditions”, Knowledge Based Systems, 92: 169-182, (2016).
• Barpanda, S.S., Majhi, B., Sa, R.K., “Region based feature extraction from non-cooperative iris images using triplet half-band filter bank”, Optics and Laser Technology, 72: 6-14, (2015).
• Bhateja, A.K., Sharma, S., Chaudhury, S., Agrawal, N., “Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm”, Pattern Recognition Letters, 73: 13- 18, (2016).
• Daugman, J., “Information theory and the iriscode”, IEEE Transactions on Information Forensics and Security, 11(2): 400-9, (2016).
• Popplewell, K., Roy, K., Ahmad, F., Shelton, J., “Multispectral iris recognition utilizing hough transform and modified lbp”, IEEE International Conference on Systems, Man and Cybernetics, 1396-1399, (2014).
• Ramkumar, R.P., Arumugam, S., “Novel iris recognition algorithm without normalization phase”, Australian Journal of Basic and Applied Sciences, 8(7): 540-46, (2014).
• Li, C., Zhou, W., Yuan, S., “Iris recognition based on a novel variation of local binary pattern”, Vision Computing, Springer, 31: 1419-1429, (2015).
• Ma, L., Tan, T., Wang, Y., Zhang, D., “Efficient iris recognition by characterizing key local variations”, IEEE Transaction Image Processing, 13(6): 739-50, (2004).
• Tajbakhsh, N., Arrabi, B.N., Soltanian-Zadeh, H., “Robust iris verification based on local and global variations”, EURASIP Journal Advance Signal Processing, 2010: 979058, (2010).
• Gragnaniello, D., Sansone, C., Verdoliva, L., “Iris liveness detection for mobile devices based on local descriptors”, Pattern Recognition Letter, 57: 81-87, (2015).
• Ojala, T., Pietikäinen, M., Harwood, D., “A Comparative study of texture measures with classification based on featured distribution”, Pattern Recognition, 29(1): 51-9, (1996).
• Reza, D., Saeed, M., Khashayar, Y., “Scale-and rotation-invariant texture description with improved local binary pattern features”, Signal Processing, 111: 274-293, (2015).
• Dan, Z., Chen, Y., Yang, Z., Wu, G., “An improved local binary pattern for texture classification”, Optik, 125: 6320-6324, (2014).
• Nanni, L., Lumini, A., Brahnam, S., “Local binary pattern variants as texture descriptors for medical image analysis”, Artificial Intelligence in Medicine, 49: 117-125, (2010).
• Nanni, L., Brahnam, S., Lumini, A., “Combining different local binary pattern variants to boost performance”, Expert Systems and Applications, 38: 6209-6216, (2011).
• Gragnaniello, D., Sansone, C., Verdoliva, L., “Iris liveness detection for mobile devices based on local descriptor”, Pattern Recognition Letters, 57: 81-87, (2015).
• He, Z., Sun, Z., Tan, T., Wei, Z., “Efficient iris spoof detection via boosted local binary patterns”, ICB 2009, Lecture Notes in Computes Science, 5558: 1080-1090, (2009).
• Hamouchene, I., Aouat, S., “A new texture analysis approach for iris recognition”, AASRI Procedia, 9: 2-7, (2014).
• Malathy, M., Selvi, J.A.V., “Spoofed iris recognition: synthesis of gabor and lbp descriptor using sppc”, Australian Journal of Basic and Applied Sciences, 8(9): 433-42, (2014).
• Tapia, J.E., Perez, C.A., Bowyer, K.W., “Gender classification from iris images using fusion of uniform local binary patterns”, Lecture Notes in Computer Science, ECCV 2014 Workshops Computer Vision, 8926: 751-763, (2015).
• Mukherjee, S., Chanda, B., “A robust human iris verification using a novel combination of features”, Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, 162-166, (2011).
• Sun, Z., Tan, T., Qiu, X., “Graph matching iris image blocks with local binary pattern”, Lecture Notes in Computer Science, Advances in Biometrics, 3832: 366-372, (2005).
• Savithiri, A., Murugan, A., “Performance analysis on half iris feature extraction using gw, lbp and hog”, International Journal of Computer Applications, 22(2): 27-32, (2011).
• Oii, C.H., Kong, N.S.P., Ibrahim, H., “Bi-Histogram equalization with a plateau limit for digital image enhancement”, IEEE Transaction Consumer Electronics, 5(4): 2072-80, (2009).
• Ojala, T., Pietikainen, M., Maenpaa, T., “Multiresolution gray scale and rotation invariant texture classification with local binary patterns”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 24(7): 971-87, (2002).
• Chang, C.C., Lin, C.J., “Libsvm: a library for support vector machines”, ACM Transactions on Intelligent Systems and Technology, 2(27): 1-27, (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
• Rui, X., “Openpr-nb”, http:// http://www.openpr.org.cn/, (2008).
• O’Connor, B., Roy, K., “Iris Recognition using level set and local binary pattern”, International Journal in Computer Theory Engineering, 6(5): 416-20, (2014).
• Tian, Q.C., Qu, H., Zhang, L., Zong, R., “Personel identity recognition approach based on iris pattern”, In Dr Juchen Yang, Editors., State of the Art of Biometrics, InTech, 2011: 163-178, (2011).
• Chengcheng, L., Weidong, Z., Shasha, Y., “Iris recognition based on a novel variation of local binary pattern”, The Visual Computer, 31(10): 1419-29, (2015).