摘要
随着现代网络和信息技术的快速发展,人们对社会信息的安全性要求日益提高,基于生物特征的智能身份鉴别方法逐渐受到广泛重视。在众多生物特征识别方法中,手背静脉识别和虹膜识别具有稳定性、防伪性和非侵犯性等优势,是生物特征识别的两大研究热点,可广泛应用于政府、军队、银行、电子商务等领域。其中,手背静脉识别是一种新兴的身份鉴别方法,其研究和应用尚处于起步阶段。虹膜识别技术在近年来得到了快速发展,相对比较成熟,但虹膜识别的核心算法仍需要进一步的研究。
本文首先建立了基于手背静脉的身份识别原型系统,围绕这一目标设计实现了近红外静脉成像装置,建立了天津大学手背静脉图像数据库,并从多分辨率纹理特征、代数特征、局部SIFT(Scale Invariant Feature Transform)特征三个方面研究了手背静脉识别算法,对算法进行性能测试,并与现有的典型算法进行对比,验证了算法的可行性和有效性。其次对虹膜识别中的关键技术进行了研究,主要包括虹膜定位、眼皮定位以及特征提取等。最后初步探讨了基于手背静脉和虹膜两种生物特征在匹配层的数据融合。
本文的创新性工作概括如下:
1、提出了基于多分辨率纹理特征分析的手背静脉识别方法。利用小波变换描述静脉纹理信息变化,实验分析了小波函数对识别性能的影响,评估了算法对手背垂直位移和旋转的容忍度。
2、提出了基于局部SIFT特征分析的手背静脉识别方法。利用SIFT特征描述手背静脉的梯度信息,并改进SIFT特征的匹配算法,从而进一步提高了识别性能。该算法对手背的平移和旋转不敏感,具有重要的实用意义。
3、提出了基于图像抽样的快速虹膜定位方法。基本思想是在抽样图像中进行粗定位,在原分辨率图像中实现精定位。利用抽样图像去除大量干扰信息,降低计算复杂度,改善了算法实时性。
4、提出了基于最大连通路径的眼皮定位方法。在确定眼皮检测区域并增强水平方向边缘后,通过标注边缘图像中的连通路径,搜索具有最大水平距离的连通路径作为眼皮边缘点实现眼皮分割,使定位速度有了很大提高。
5、提出将手背静脉和虹膜两种生物特征相结合进行身份识别。分别利用D-S证据理论和支持向量机方法在匹配层进行了融合实验和分析,融合后识别性能得到了较大提高。
With the rapid development of modern network and information technology, the safety requirements for social information are constantly increased, and personal identification based on biometrics has received extensive attention. In the biometrics family, hand vein and iris recognitions are characteristic by stability, security, and un-intrusion, which are two research focuses and can be widly applied to many fields such as government, military, bank and electronic commerce. Hand vein recognition is a newly emerging identification technology, and its study and application are at the preliminary stage. Iris recognition technology has developed fast and become mature in recent years, whereas further study about kernel algorithms is still needed.
Firstly a hand vein recognition prototype system is developed. Centering on this goal, we design a near-infrared (NIR) hand vein imaging system and construct Tianjin Universty (TJU) hand vein database. Three hand vein recognition methods are developed based on the multi-resolution texture feature, algebraic feature and local SIFT feaure. The proposed recognition methods for hand vein are tested and compared with the classical methods, which demonstrate their feasibility and effectiveness. Then the key iris algorithms are studied including iris localization, eyelid localization and iris feature extraction. Eventually, the paper preliminarily discusses the multi-biometric fusion based on the hand vein and iris at the match score level.
The major innovations of the dissertation are as follows:
1. A hand vein recognition method based on multi-resolution texture feature analysis is proposed. Wavelet transform is applied to describe the vein texture variety, the influence of wavelet function on recognition performance is analyzed by experiments, and the tolerance to hand shift and rotation is evaluated.
2. A hand vein recognition method based on local SIFT feature is developed. The SIFT feature is adopted to describe the gradient information of hand vein, and the improved matching method for SIFT features enhances the identification performance. This algorithm is not sensitive to hand shift and rotation, which makes it has important practical significance.
3. A rapid iris localization algorithm based on image sampling is advanced. Its basic idea is to locate the iris coarsely in the sampled image, and achieve the exact localization in the image with original resolution. Much disturbing information can be removed to reduce the computation complexity, and the real-time performance is improved greatly.
4. An eyelid detection method based on maximal connection path is developed. After determining the eyelid detection region and enhancing the horizontal boundary, the maximal connection path is searched as the eyelid edge points through labeling the connected paths in the edge image, and then the eyelid segmentation can be achieved. This method increases the detection speed largely.
5. The hand vein and iris recognitions are combined for personal identification. The D-S evidence theory and support vector machines (SVM) are applied to fusion experiment and analysis at the match score level, and the recognition performance has been improved greatly.
引文
[1] K. Jain, A. Ross, S. Prabhakar. An introduction to biometric recognition. IEEE Trans. Circuits Syst. Vidio Technol., 2004, 14(1): 4-20.
[2] R. de Luis-García, C. Alberola-López, O. Aghzout, J. Ruiz-Alzola. Biometric identification systems. Signal Processing, 2003, 83(12): 2539-2557.
[3] L. Ma, T. Tan, Y. Wang, D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Processing, 2004, 13(6): 739-750.
[4] S. Prabhakar, S. Pankanti, A. K. Jain. Biometrics recognition: security and privacy concerns. IEEE Security & Privacy, 2003, 1(2): 33-42.
[5]生物特征识别简介. http://b2museum.cdstm.cn/identification/swtzzb-1.htm.
[6]身份识别:解惑安全难题.金融时报, 2008.
[7]“汉王证照识别系统”电信行业解决方案. http://www.mie168.com/other/2004-10/44010.htm.赛迪网, 2004.
[8] K. Jain, R. M. Bolle, S. Pankanti. Biometrics: Personal identification in a networked society. Norwell, Kiuwer, New York, 1999.
[9] J. L. Wayman. Fundamentals of biometric authentication technologies. International Journal of Image and Graphics, 2001, 1(1): 93-113.
[10]刘铁根.光学防伪检测技术.北京:电子工业出版社出版, 2008, 265-359.
[11] M. S. Kim, D. Kim, S. Y. Lee. Face recognition using the embedded HMM with second-order block-specific observations. Pattern Recognition, 2003, 36(11): 2723-2735.
[12] L. O'Gorman. An overview of fingerprint verification technologies. Information Security Technical Report, 1998, 3(1): 21-32.
[13] W. W. Boles. A security system based on human iris identification using wavelet transform. Engineering Applications of Artificial Intelligence, 1998, 11(1): 77-85.
[14] C. C. Han, H. L. Cheng, C. L. Lin, K. C. Fan. Personal authentication using palm-print features. Pattern Recognition, 2003: 36(2): 371-381.
[15] M. A. Ismail, S. Gad. Off-line arabic signature recognition and verification. Pattern Recognition, 2000, 33(10): 1727-1740.
[16] E. L. J. Bohez, T. R. Senevirathne. Speech recognition using fractals. Pattern Recognition, 2001, 34(11):2227-2243.
[17] M. S. Nixon, J. N. Carter, J. D. Shutler, M. G. Grant. New advances inautomatic gait recognition. Information Security Technical Report, 2002, 7(4): 23-35.
[18] H. Faulds. On the skin-furrows of the hand. Nature, 1880, 22: 605-605.
[19] A. Sheldon. The future of forensic computing. Digital Investigation, 2005, 2(1): 31-35.
[20] R. Davis. Finger points to biometric myths. Infosecurity Today, 2004, 1(4): 44-45.
[21] J. Daugman. Iris Recognition. American Scientist, 2001, 89(4): 326-333.
[22] H. Chan, W. W. Bledsoe. A man-machine facial recognition system—some preliminary results. Panoramic Research Inc., Palo Alto, CA, 1965.
[23] P. McKerrow, K. K. Yoong. Classifying still faces with ultrasonic sensing. Robotics and Autonomous Systems, 2007, 55(9): 702-710.
[24] B. Moghaddam, T. Jebara. Bayesian face recognition. Pattern Recognition, 2000, 33(11): 1771-1782.
[25] P. MacGregor, R.Welford. Veincheck: imaging for security and personnel identification. Adv. Imaging, 1991, 6(7): 52-56.
[26]田捷,杨鑫.生物特征识别技术理论与应用.北京:电子工业出版社, 2005, 11-15.
[27]齐永锋,火元莲,张家树.生物特征识别的关键技术与安全性思考.微计算机信息, 2008, 24(18): 30-31, 34.
[28]生物特征识别技术,缔造安全空间. http://www.probuck-tech.com/web/FingerprintPage.aspx?sfortid=2.
[29] B. Zhu, S. Liu, Q. Ran. Optical image encryption based on multifractional fourier transforms. Optics Leters, 2000, 25(16): 1159-1161.
[30] S. Mallat, W. L. Hwang. Singularity detection and processing with wavelets. IEEE Trans. On Information Theory, 1992, 38(2): 617-643.
[31] J. Daugman. Complete discrete 2-D gabor transform by neural networks for image analysis and compression. IEEE Trans on ASSP, 1988, 36(7): 1169-1179.
[32] L. Wang, G. Leedham, S.-Y. Cho. Infrared imaging of hand vein patterns for biometric purposes. Computer Vision, 2007, 1(3-4): 113-122.
[33] S. Zhao, Y. Wang, Y. Wang. Extracting hand vein patterns from low-quality images: a new biometric technique using low-cost devices. Fourth International Conference on Image and Graphics, 2007, 667-671.
[34] L. Wang, G. Leedham, D. Siu-Yeung Cho. Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern Recognition, 2008, 41(3): 920-929.
[35]静脉识别系统. http://www.biocn.com/vein-300s.htm.
[36] S. K. Im, H. M. Park, Y. W. Kim, S. C. Han, S. W. Kim, C. H. Kang. A biometric identification system by extracting hand vein patterns. Journal of the Korean Physical Society, 2001, 38(3): 268-272.
[37] M. Shahin, A. Badawi, M. Kamel. Biometric authentication using fast correlation of near Infrared hand vein patterns. International Journal of Biomedical Sciences, 2007, 2(3): 141-148.
[38]生物识别技术设备. http://szcecv.cps.com.cn/products/detail.jsp?id=49133, 2008.
[39]林喜荣,庄波,苏晓生,周云龙,包桂秋.人体手背血管图像的特征提取及匹配.清华大学学报(自然科学版), 2003, 43(2): 164:167.
[40]周斌,林喜荣,贾惠波.多分辨率滤波在手背血管特征提取中的应用.计算机辅助设计与图形学学报, 2006, 18(1): 41:45.
[41]丁宇航.人体手背静脉识别算法研究.哈尔滨工程大学博士学位论文.
[42]王科俊,丁宇航,庄大燕,王大振.手背静脉阈值分割.自动化技术与应用, 2005, 24(8):19-22.
[43] L. Flom, A. Safir. Iris recognition system. United States, Patent, No.4641349, 1987.
[44]虹膜识别——前景广阔的生物认证技术. http://www.c-ps.net/trade/content/2008/10/8457.html. 2008.
[45]中国科技界开始发力生物识别技术鉴定个人身份. http://news.jxnews.com.cn/system/2003/09/23/000521945.shtml. 2003.
[46]中科虹霸嵌入式虹膜识别仪. http://www.irisking.com/html/chanpinjieshao/hongmoshibiechanpin/20070117/19.html. 2007.
[47] L. Hong, A. K. Jain, S. Pankanti. Can multibiometrics improve performance? In Proceedings autoID, 1999, 59-64.
[48] R. Brunelli, D. Falavigna. Person Identification Using Multiple Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(10): 955-966.
[49] G. Chetty, M. Wagner. Robust face-voice based speaker identity verification using multilevel fusion. Image and Vision Computing, 2008, 49(9): 995-1000.
[50] A. F. Abate, R. Distasi, M. Nappi, D. Riccio. Face authentication using speed fractal technique Image and Vision Computing, 2006, 24(9): 977-986.
[51] C. Sanderson, K. K. Paliwal. Identity verification using speech and face information. Digital Signal Processing, 2004, 14(5): 449-480.
[52] P. Jourlin, J. Luettin, D. Genoud, H. Wassner. Acoustic-labial speakerverification. Pattern Recognition Letters, 1997, 18: 853-858.
[53] P. Jourlin, J. Luettin, D. Genoud, H. Wassner. Integrating acoustic and labial information for speaker identification and verification. 5th European Conf. Speech Communication and Technology, 1997, 3: 1603-1606.
[54] K. Jain, R. P. W. Duin, J. Mao. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(1): 4-37.
[55] G. Shakhnarovich, T. Darrell. On probabilistic combination of face and gait cues for identification. In Proc. FGR’02, 2002, 169-174.
[56] A. Kale, A. K. Roychowdhury, R. Chellappa. Fusion of gait and face for human identification. In Proc. ICASSP’04, 2004, 901-904.
[57] Y. Wang, T. Tan, A. K. Jain. Combining Face and Iris Biometrics for Identity Verification. Lecture Notes in Computer Science, Springer Berlin Heidelberg, 2003.
[58] B. Son, Y. Lee. The Fusion of Two User-friendly Biometric Modalities: Iris and Face. IEICE Transactions on Information and Systems, 2006, E89-D(1): 372-376.
[59]王风华,韩九强,姚向华.一种基于虹膜和人脸的多生物特征融合方法.西安交通大学学报, 2008, 42(2): 1337-137.
[60]甘俊英,高建虎,李春芝.基于人脸与虹膜的融合算法研究.计算机工程与设计, 2008, 29(3): 683-686.
[61]王明时,诸强等.数字血管减影系统的研究.天津大学学报, 2002, 35(1): 120-125.
[62]鲁思慧.现代医用超声成像系统解决方案.常规医疗装备, 2005, 4(6): 16-20.
[63] C. L. Lin, K. C. Fan. Biometric verification using thermal images of palm-dorsa vein patterns. IEEE Transactions on Circuits and Systems for Video Technology, 2004, 14(2): 199-213.
[64] S. Fantini, M. A. Franceschini. Frequency-domain techniques for tissue spectroscopy and imaging. Handbook of optical biomedical diagnostics (SPIE Press, Bellingham), 2002, 405-453.
[65]赵军,丁海曙,腾轶超.频域近红外光谱方法定量测量组织氧饱和度.光子学报, 2005, 34(3): 386-389.
[66] M. Shahin, A. Badawi, M. Kamel. Biometric authentication using fast correlation of near infrared hand vein patterns. International Journal of Biomedical Sciences, 2007, 2(3): 141-148.
[67] T. Tanaka, N. Kubo. Biometric authentication by hand vein patterns. In Proc. SICE Annual Conference in Sapporo, 2004, 249-253.
[68] J. M. Cross, C. L. Smith. Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification. 29th International Carnahan Conference on Security Technology, 1995, 20-35.
[69] N. Miura, A. Nagasaka, T. Miyatake. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications, 2004, 15: 194-203.
[70]张会林,简献忠.人体手背静脉血管图像增强处理算法研究.仪器仪表学报, 2005, 26(8): 729-731.
[71] H. Tamura, S. Mori, T. Yamawaki . Textural Features Corresponding to Visual Perception. IEEE Transactions on Systems, Man and Cybernetics, 2007, 8(6): 460-473.
[72] S. Kulkarni, B. Verma. Fuzzy logic based texture queries for CBIR. Fifth International Conference on Computational Intelligence and Multimedia Applications, 2003, 223-228.
[73] R. M. Haralick, K. Shanmugam, I. Dinstein. Texture feature for image classification. IEEE Transactions on System, Man, and Cybernetics, 1973, SMC-3(6): 768-780.
[74] S. Sidhu, K. Raahemifar. Texture classification using wavelet transform and support vector machines. IEEE CCECE/CCGEI, Saskatoon, 2005, 941-944.
[75]刘文耀.光电图像处理(第1版).北京:电子工业出版社, 2002, 278-317.
[76]苪国胜,康健.小波与傅里叶分析基础.北京:电子工业出版社, 2005, 170-195.
[77]秦定宇,王敬东,李鹏.图像融合中小波基的选择分析.光电子技术, 2006, 26(3): 203-207.
[78]柯丽,黄廉卿.适于遥感图像实时压缩的小波基的选择.光学技术, 2005, 31(1): 77-80,83.
[79] M. Kirby, L. Sirovich. Application of the Karhunen-Lo&e Procedure for the haracterization of Human Faces. IEEE Transactions on Pattern Analysis and Machine, 1990, 12(1): 103-108.
[80] M. Turk, A. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 72-86.
[81] M. Turk, A. Pentland. Face Recognition Using Eigenfaces. IEEE Comput. Soc. Conf. on CVPR, 1991., 586-591.
[82] H. C. Kim, D. Kim, S. Yang Bang. A numeral character recognition using the PCA mixture model. Pattern Recognition Letters, 2002, 23(1-3): 103-111.
[83]王世晞,贺志国.基于PCA特征的快速SAR图像目标识别方法.国防科技大学学报, 2008, 30(3): 136-140.
[84] L. W. Zhao, S. W. Luo, L. Z. Liao. 3D object recognition and pose estimation using kernel PCA. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 5: 3258-3262.
[85] J. Lu, K. N. Plataniotis. Face recognition using LDA-based algorithms. IEEE Transactions on Neural Networks, 2003, 14(1): 195:200.
[86] S. S. Wilks. Mathematical Statistics. New York: Wiley, 1962.
[87] R. Duda, P. Hart. Pattern Classification and Scence Analysis. New York: Wiley, 1973.
[88] D. Swets, J. Weng. Using Discriminant Eigenfeatures for Image Retrieval. IEEE Transactions on Patern Analysis and Machine Intelligence, 1996, 18(8): 831-836.
[89] T. Kawatani, H. Shimizu. Handwritten Kanji recognition with the LDA method. Fourteenth International Conference on Pattern Recognition, 1998, 2: 16-20.
[90]苏菡,黄凤岗,洪文.一种基于运动分析的步态识别方法.哈尔滨工程大学学报, 2006, 27(3): 418-422.
[91] Z. Q. Hong, J. Y. Yang. Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition, 1991, 24 (4): 317-324.
[92] C. Liu, H. Wechsler. A shape and texture based Enhanced Fisher classifier for face recognition. IEEE Transactions on Image Processing, 2001, 10(4): 598-608.
[93] L. Chen, H. Liao, M. Ko, J. Lin, G. Yu. A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recognition, 2000, 33 (10): 1713-1726.
[94] H. P. Moravec. Visual mapping by a robot rover. 6th International Joint Conference on Artificial Intelligence, 1979, 599-601.
[95] C. Harris, M. Stephens. A combined comer and edge detector. Alvey Vision Conference, 1988, 147-151.
[96] K. Mikolajczyk, C. Schmid. An affine variant interest point detector. In European Conference on Computer Vision, 2002, 25(3): 128-142.
[97] K. Mikolajczyk, C. Schmid. Scale & Affine Invariant Interest Point Detectors. International Journal of Computer Vision, 2004, 60(1): 63-68.
[98] T. Tuytelaars, L. Van Gool, L. D'haene, R. Koch. Matching of Affinely Invariant Regions for Visual Servoing. IEEE International Conference onRobotics and Automation, 1999, 2(2): 1601-1606.
[99] J. Matas, O. Chum, M. Urban, T. Pajdla. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. 13th British Machine Vision Conf., 2002, 384-393.
[100] D. G. Lowe. Local feature view clustering for 3D object recognition. IEEE Conference on Computer Vision and Pattern Recognition, 2001, 22(6): 682-688.
[101]李晓明,郑链,胡占义.基于SIFT特征的遥感影像自动配准.遥感学报, 2006, 10(6): 885-892.
[102] P. Moreno, M. J. Mar′?n-Jim′enez, A. Bernardino, J. Santos-Victor, N. P. de la Blanca. A comparative study of local descriptors for object category recognition: SIFT vs HMAX. In Lecture Notes in Computer Science, Springer-Verlag, Berlin, 2007.
[103] K. Mikolajczyk and C. Schmid. A Performance Evaluation of Local Descriptors. IEEE Trans. Pattern Anal. Machine Intell. 2005, 27(10): 1615-1630.
[104] H. A. Park, K. R. Park. Iris recognition based on score level fusion by using SVM. Pattern Recognition Letters, 2007, 28: 2019-2028.
[105] J. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions On Pattern Analysis and Machine Intelligence, 1993, 15(11): 1148-1161.
[106]姚鹏,叶学义,庄镇泉,吴亮,龙飞,李斌.一种新颖的虹膜图像预处理算法.中国科学技术大学学报, 2006, 36(8): 806-811.
[107] H. Huang, G. Hu. The research and application of iris recognition algorithm. IEEE Journal of Quantum Electronics, 2002, 10: 404-409.
[108] R. P. Wildes. Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 1997(85): 1348-363.
[109]王琪,费耀平.基于边缘追踪的虹膜定位算法.信息技术, 2005, 29(9): 41-43.
[110] C. Wang, H. Ye. The research on the algorithm of iris recognition. Journal of Guizhou University of Technology, 2000, 29(3): 48-52.
[111] L. Ma, Y. Wang, T. Tan. Iris recognition using circular symmetric filters. IEEE Proceeding of Iternational Conference on Pattern Recognition, 2002(2): 414-417.
[112] T. Camus, R. Wildes. Reliable and fast eye finding in close-up images. Proceedings of the IEEE International Conference on Pattern Recognition, 2002, 1: 389-394.
[113]李霞,于鲁义,王娜.基于直线检测的虹膜快速定位方法.计算机辅助设计与图形学学报, 2006, 18(8): 1155-1159.
[114] C. Tisse, L. Martin, L. Torres, M. Robert. Person identification technique using human iris recognition. In Proceedings of ICVI’02, 2002, 294-299.
[115] Z. He, T. Tan, Z. Sun. Iris Localization via Pulling and Pushing. The 18thInternational Conference on Pattern Recognition, 2006, 4: 366-369.
[116]陈世哲,刘国栋,胡涛等. IC芯片视觉检测中快速图像匹配定位.光电子.激光, 2005, 16(11): 1346-1349.
[117]董银文,郭雷,姚俊.一种从噪声图像中提取边缘的启发式搜索算法.模式识别与人工智能, 2006, 19(1): 14-19.
[118] J. Daugman. How iris recognition works. IEEE Transactions on Circuit and System for Video Technology, 2004, 14(1): 21-30.
[119]姚鹏.复杂条件下的虹膜识别算法的研究.中国科学技术大学博士论文, 2005.
[120] X. Liu, K. W. Bowyer, P. J. Flynn. Experiments with an improved iris segmentation algorithm. Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05), 2005, 118-123.
[121]王林,练秋生.虹膜定位算法研究.电视技术, 2006, 7: 77-79.
[122] W. Sankowski, K. Grabowski, M. Napieralska, M. Zubert. Eyelids localization method designed for iris recognition system. 14th International Conference on Mixed Design of Integrated Circuits and Systems, 2007, 622-627.
[123] Y. Wu, H. Liu, H. Zha. A new method of detecting human eyelids based on deformable templates. International Conference on Systems, Man and Cybernetics, 2004, 7: 604-609.
[124] M. Pardas. Extraction and Tracking of the Eyelids. Proceedings of IEEE International Conference on Acoustics, 2000, 2357-2360.
[125]王植,贺赛先.一种基于Canny理论的自适应边缘检测方法.中国图象图形学报, 2004, 9(8): 957-962.
[126] E. M. Arvacheh, H. R Tizhoosh. Iris Segmentation: Detecting Pupil, Limbus and Eyelids. 2006 IEEE International Conference, 2006, 2453-2456.
[127] C. H. Morimoto, T. T. Santos, A. S. Muniz. Automatic iris segmentation using active near infrared lighting. XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05), 2005, 37-43.
[128] L. Yu, D. Zhang, K. Wang. The relative distance of key point based iris recognition. Pattern Recognition, 2007, 40: 423-430.
[129] J. Cui, Y. Wang, T. Tan, L. Ma, Z. Sun. A fast and robust iris localization method based on texture segmentation. SPIE Defense and Security Symposium, 2004, 5404: 401-408.
[130]来毅,路陈红,卢朝阳.用于虹膜识别的眼睑及眼睫毛遮挡检测.计算机辅助设计与图形学学报, 2007, 19(3): 346-350.
[131]王蕴红,朱勇,谭铁牛.基于虹膜识别的身份鉴别.自动化学报, 2002, 28(1): 1-10.
[132] W. W. Boles, B. Boashah. A human identification technique using images of the iris and wavelet transform. IEEE Trans. on Signal Processing, 1998, 46: 1185-1188.
[133] D. Martin-Roche, C. Sanchez-Avila, R. Sanchez-Reillo. Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. 35th IEEE International Conference on Security Technology, 2001, 272-277.
[134] R. P. Wildes. Iris recognition: An emerging biometric technology. Proceedings of the IEEE, 1997: 1348-1363.
[135] L. Ma, T. Tan, Y Wang, D. Zhang. Personal identification based on iris texture analysis. IEEE Transactions on Patern Analysis and Machine Intelligence, 2003, 25(12): 1519-1533.
[136] L. Ma, T. Tan, Y. W ang, D. Zhang. Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Processing, 2004, 13(6): 739-750.
[137] J. Kittler, M. Hatef, RPW. Duin, J. Matas. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3): 226-239.
[138] C. Sanderson, K. K. Paliwal. Identity verification using speech and face information. Digital Signal Processing, 2004, 14: 449-480.
[139] D. M. Tax, M. Breukelen, P.W. duin, J. Kittler. Combining multiple classifiers by avera ging or by multiplying? Pattern Recognition, 2000, 33(9): 1475-1485.
[140] K. Jain, Arun Ross. Learning user-specific parameters in a multibiometric system. Proc. International Conferenceon Image Processing (ICIP), 2002, l: 57-60.
[141] P. Verlinde, P. Druyts, G. Chollet, M. Achercy. Applying the naive Bayes classifier for decision fusion in a multi-modal identity verification system. International Symposium In Memoriam Pierre Devijver, Brussels (Belgium), 1999.
[142] A. P. Dempster. Upper and Lower Probabilities Induced by Multivalued Mapping. Annals of Mathematical Statistics, 1967, 38(2): 325-339.
[143] L. A. Zadeh. Fuzzy Sets. Information and Control, 1965, 8: 338-356.
[144] Y. Linde, A. Buzo, R. Gray. An algorithm of vector quantizer design. IEEE Trans. on Communication, 1980, 23(1): 84-95.
[145] C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 1995, 20: 273-297.
[146]张浩,蔡晋辉,周泽魁. DS证据理论在SAR图像边缘检测中的应用.武汉大学学报:信息科学版, 2008, 1: 105-108.
[147] L. Rujie, Y. Baozong. A D-S based multi-channel information fusion method using classifier’s uncertainty measurement. 5th International Conference onSignal Processing, 2000, 2(2): 1297-1300.
[148]边肇祺,张学工.模式识别(第2版).北京:清华大学出版社, 2000, 286-304.
[149] X. Chen, Y. Li, R. Harrison, YQ. Zhang. Type-2 fuzzy logic-based classifier fusion for support vector machines. Applied Soft Computing, 2008, 8: 1222-1231.
[150] M. H. Fatemi, S. Gharaghani, S. Mohammadkhani, Z. Rezaie. Prediction of selectivity coefficients of univalent anions for anion-selective electrode using support vector machine. Electrochimica Acta, 2008, 53: 4276-4282.
