{"references": ["J. G. Daugman, \"High confidence visual recognition of persons by a test\nof statistical independence,\" IEEE Trans. Pattern Anal. Machine Int.,\nvol. 15, no. 11, 1993, pp. 1148\u20131161.", "J. G. Daugman, \"How iris recognition works,\" IEEE Trans. Circuits\nSystems Video Technol., vol. 14, no. 1, 2004, pp. 21\u201329.", "P. W. Hallinan, \"Recognizing human eyes,\" Geometric Methods in\nComputer Vision, vol. 1570, pp. 214\u2013226, 1991.", "K. W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for\niris biometrics: a survey, Computer Vision Image Understanding, vol.\n110, no. 2, 2008.", "H. Proen\u00e7a, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, \"The\nUBIRIS.v2: A database of visible wavelength iris images captured onthe-\nmove and at-adistance,\" IEEE Trans. Pattern Anal. Machine Intell.,\nvol. 32, 2010, pp. 1529\u20131535.", "J. Daugman, \"New methods in iris recognition,\" IEEE Trans. Systems,\nMan, Cybernet., Part B vol. 37, 2007, pp. 1167\u20131175.", "Z. Sun and T. Tan, \"Ordinal measures for iris recognition,\" IEEE Trans.\nPattern Anal. Machine Intell., vol. 31, 2009, pp. 2211\u20132226.", "P. Li and H. Ma, \"Iris recognition in non-ideal imaging conditions,\"\nPattern Recognition Letters, vol. 33, no. 8, 2012, pp. 1012\u20131018.", "H. Proen\u00e7a, L.A. Alexandre, Iris segmentation methodology for\nnoncooperative recognition, IEEE Proceedings of the Vision, Image and\nSignal Processing, vol. 153, no. 2, 2006, pp. 199\u2013205.\n[10] J. G. Daugman, New methods in iris recognition, IEEE Transactions on\nSystem, Man, and Cybernetics\u2013Part B: Cybernetics, vol. 37, no. 5, 2007,\npp. 1167\u20131175.\n[11] K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, \"Image understanding\nfor iris biometrics: A survey,\" Computer Vision and Image\nUnderstanding, vol. 110, 2008, pp. 281\u2013307.\n[12] H. Proen\u00e7a, L.A. Alexandre, The NICE.I: noisy iris challenge evaluation\n\u2013 part I, in: Proceedings of the of First International Conference on\nBiometrics: Theory, Applications, and Systems, 2007, pp. 1\u20134.\n[13] H. Proen\u00e7a, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, \"The\nUBIRIS.v2: A database of visible wavelength iris images captured onthe-\nmove and at-a-distance,\" IEEE Trans. Pattern Anal. Machine Intell.,\nvol. 32, 2010, pp. 1529\u20131535.\n[14] Y. H. Tsai, \"A Weighted Approach to Unconstrained Iris Recognition,\"\nin Proc. International Conference on Computer Science and Intelligent\nSystems, London, UK, 2014.\n[15] Iris image database. http://socia-lab.di.ubi.pt/\n[16] Y. Moses, S. Edelman, and S. Ullamn, \"Generalization on Novel Images\nin Upright and Inverted Faces,\" Perception, vol. 25, 1996, pp. 443-461.\n[17] P. J. Phillips and Y. Vardi, \"Efficient Illumination Normalization of\nFacial Images,\" Pattern Recognition Letters, vol. 17, 1996, pp. 921-927.\n[18] P. Viola, M. J. Jonse, \"Robust Real-Time Face Detection,\" International\nJournal of Computer Vision, vol. 57, no. 2, 2004, pp. 137-154.\n[19] Y. Freund and R. E. Schapire, \"Experiments with a New Boosting\nAalgorithm,\" in Proc. 13th International Conference on Machine\nLearning, Bari, Italy, 1996, pp. 148-156.\n[20] R. E. Schapire and Y. Singer, \"Improved Boosting Algorithms Using\nConfidence-rated Predictions.\" Machine Learning, vol. 37, no. 3, 1999,\npp. 297-336.\n[21] J. Friedman, T. Hastie, and R. Tibshirani, \"Additive Logistic Regression:\na Statistical View of Boosting,\" The Annals of Statistics, vol.28, no.2,\n2000, pp. 337-407.\n[22] L. L. Huang, A. Shimizu, \"A multi-expert approach for robust face\ndetection,\" Pattern Recognition, vol. 39, 2006, pp. 1695-1703.\n[23] T. Ojala, M. Pietikainen, and T. Maenpaa, \"Multiresolution gray-scale\nand rotation invariant texture classification with local binary patterns,\"\nIEEE Trans. on PAMI, vol. 24, no. 7, 2002, pp. 971\u2013987.\n[24] V. Vezhnevets, V. Sazonov and A. Andreeva, \"A Survey on Pixel-Based\nSkin Color Detection Techniques\" Proc. of Graphicon conference, 2003.\n[25] G. Pajares and J. M. de la Cruz\" A wavelet-based image fusion tutorial\",\nPattern Recognition Volume 37, Issue 9, September (2004), pp. 1855-\n1872"]}

This paper presents an efficient fusion algorithm for iris images to generate stable feature for recognition in unconstrained environment. Recently, iris recognition systems are focused on real scenarios in our daily life without the subject’s cooperation. Under large variation in the environment, the objective of this paper is to combine information from multiple images of the same iris. The result of image fusion is a new image which is more stable for further iris recognition than each original noise iris image. A wavelet-based approach for multi-resolution image fusion is applied in the fusion process. The detection of the iris image is based on Adaboost algorithm and then local binary pattern (LBP) histogram is then applied to texture classification with the weighting scheme. Experiment showed that the generated features from the proposed fusion algorithm can improve the performance for verification system through iris recognition.