自适应PSO融合的多模态生物特征识别方法
|
摘要
生物特征识别技术是利用个体所固有的生理和行为特征来进行身份鉴定的技术。与传统的身份验证手段相比，生物特征识别技术具有防伪性好、便于携带、不易丢失、不易遗忘的优点。虽然单一的生物特征识别技术(单模态)具有众多优点，但是每种单模态识别的准确率是有限的，都存在各自的缺点，适合应用于不同的场合。多模态生物特征识别是融合多种生物特征对个体进行身份验证的技术。通过多生物特征融合的方法，可以提高生物特征识别系统的准确率等性能，因此具有广阔的应用前景，是当前生物特征识别领域研究的热点。
本文提出了自适应PSO融合算法，在决策层上解决多生物特征融合的问题。在本文提出的方法中，多模态的融合被构造成贝叶斯决策融合的形式，自适应PSO融合算法可以最小化融合系统的贝叶斯风险，搜索得到最优的多模态融合决策规则，从而构造出一个最优的多模态融合系统。为了提高融合算法的性能，本文进一步提出了最小速率限制的二进制PSO算法AIS-MVLBPSO，该算法采用最小速率阈值对PSO颗粒的搜索速率进行限制，从而可以有效改进算法的收敛能力。
为了验证自适应PSO融合算法的有效性，本文使用自适应PSO融合算法融合人脸和指纹两种单模态的生物特征识别方法，并在ORL、UMIST人脸数据库以及MCYT指纹数据库上设计实现了对比实验。实验表明，多模态生物特征识别系统的性能要优于人脸、指纹两种单模态识别系统的性能。自适应PSO融合算法可以根据单模态识别方法的性能差异，自动选择最优的融合决策规则来最优化多模态系统的识别结果。根据自适应PSO融合算法，本人设计实现了多模态生物特征识别系统MultiBIS，本文对MultiBIS系统的设计框架、系统实现、系统性能等作了详细介绍。
Biometrics is the technology which refers to identifying an individual based on hisor her physiological or behavioral characteristics. Compared to traditional identificationand verification methods, biometrics is more convenient for users, reduces fraud, andcan not be forgotten or replaced. Biometrics has been proven to be successful and hasbeen applied in some fields, however, each single biometric modality (unimodal) has itsadvantages as well as drawbacks, and the error rates associated with unimodal biometricsystem are quite high which makes them unacceptable for deployment in securitycritical applications. Some of the problems that affect unimodal biometric system can bealleviated by using multimodal biometric traits. Systems that fuse multiple cuesobtained from two or more biometric indicators for the purpose of person recognitionare called multimodal biometric systems. Multimodal strategy and fusion scheme cansignificantly improve the overall accuracy of the biometric system. Multimodalbiometrics has been receiving a lot of attention in the recent years.
This dissertation presents an Adaptive Particle Swarm Optimization Fusion(APSOF) algorithm which can fuse multiple biometric modalities at the decision level.The fusion problem is designed as a Bayesian decision framework and the APSOFalgorithm can automatically adjust the optimum decision fusion rules to minimize theBayesian error cost for the fusion system. To improve the performance of the fusionalgorithm, this thesis also proposes a novel Minimum Velocity Limited PSO. Theminimum velocity strategy applies a threshold to control the flying velocities of PSOparticles, thus improves the convergence ability and stability of the algorithm.
To demonstrate the performance of APSOF algorithm, this dissertation uses thisfusion scheme to fuse two biometric modalities face and fingerprint. The experimentsare designed and carried out on the ORL, UMIST face database and MCYT fingerprintdatabase. Experimental results show that multimodal fusion scheme outperformsunimodal system based on face or fingerprint. It is proven that APSOF algorithm canselect the optimum decision fusion rules adaptively according to the variation of theaccuracy of unimodal system. Based on APSOF algorithm, I developed a Multimodal Biometric Identification System (MultiBIS). This thesis describes the design,implementation and functions of MultiBIS.
本文提出了自适应PSO融合算法，在决策层上解决多生物特征融合的问题。在本文提出的方法中，多模态的融合被构造成贝叶斯决策融合的形式，自适应PSO融合算法可以最小化融合系统的贝叶斯风险，搜索得到最优的多模态融合决策规则，从而构造出一个最优的多模态融合系统。为了提高融合算法的性能，本文进一步提出了最小速率限制的二进制PSO算法AIS-MVLBPSO，该算法采用最小速率阈值对PSO颗粒的搜索速率进行限制，从而可以有效改进算法的收敛能力。
为了验证自适应PSO融合算法的有效性，本文使用自适应PSO融合算法融合人脸和指纹两种单模态的生物特征识别方法，并在ORL、UMIST人脸数据库以及MCYT指纹数据库上设计实现了对比实验。实验表明，多模态生物特征识别系统的性能要优于人脸、指纹两种单模态识别系统的性能。自适应PSO融合算法可以根据单模态识别方法的性能差异，自动选择最优的融合决策规则来最优化多模态系统的识别结果。根据自适应PSO融合算法，本人设计实现了多模态生物特征识别系统MultiBIS，本文对MultiBIS系统的设计框架、系统实现、系统性能等作了详细介绍。
Biometrics is the technology which refers to identifying an individual based on hisor her physiological or behavioral characteristics. Compared to traditional identificationand verification methods, biometrics is more convenient for users, reduces fraud, andcan not be forgotten or replaced. Biometrics has been proven to be successful and hasbeen applied in some fields, however, each single biometric modality (unimodal) has itsadvantages as well as drawbacks, and the error rates associated with unimodal biometricsystem are quite high which makes them unacceptable for deployment in securitycritical applications. Some of the problems that affect unimodal biometric system can bealleviated by using multimodal biometric traits. Systems that fuse multiple cuesobtained from two or more biometric indicators for the purpose of person recognitionare called multimodal biometric systems. Multimodal strategy and fusion scheme cansignificantly improve the overall accuracy of the biometric system. Multimodalbiometrics has been receiving a lot of attention in the recent years.
This dissertation presents an Adaptive Particle Swarm Optimization Fusion(APSOF) algorithm which can fuse multiple biometric modalities at the decision level.The fusion problem is designed as a Bayesian decision framework and the APSOFalgorithm can automatically adjust the optimum decision fusion rules to minimize theBayesian error cost for the fusion system. To improve the performance of the fusionalgorithm, this thesis also proposes a novel Minimum Velocity Limited PSO. Theminimum velocity strategy applies a threshold to control the flying velocities of PSOparticles, thus improves the convergence ability and stability of the algorithm.
To demonstrate the performance of APSOF algorithm, this dissertation uses thisfusion scheme to fuse two biometric modalities face and fingerprint. The experimentsare designed and carried out on the ORL, UMIST face database and MCYT fingerprintdatabase. Experimental results show that multimodal fusion scheme outperformsunimodal system based on face or fingerprint. It is proven that APSOF algorithm canselect the optimum decision fusion rules adaptively according to the variation of theaccuracy of unimodal system. Based on APSOF algorithm, I developed a Multimodal Biometric Identification System (MultiBIS). This thesis describes the design,implementation and functions of MultiBIS.
引文
[1] Hong L., Jain A. K. Integration Faces and Fingerprints for Personal Identification. IEEE Transaction Pattern Analysis and Machine Intelligence, 1998, 20(12), 1295-1300.
[2] Ben-Yacoub S., Abdeljaoued Y., Mayoraz E. Fusion of Face and Speech Data for Person Identity Verification. Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAPj, 1999.
[3] Souheil Ben-Yacoub, etc. Fusion of Face and Speech Data for Person Verification. IEEE Transaction on Neural Networks, 1999, vol. 10(5): 1065-1074.
[4] Ross A. and Jain A. K. Information Fusion in Biometrics, Pattern Recognition Letters, September, 2003, Vol. 24, Issue 13, pp. 2115-2125.
[5] Turk M. A., Pentland A. P. Eigenfaces for recognition, J. Cognitive Neurosci., 1991, vol. 3, no. 1, pp. 71-86.
[6] Hong L., Jain A. K. Integrating Faces and Fingerprints for Personal Identification. IEEETruns. on Portern Anal. and Machine Inrell., 1998, vol. 20, no. 12, pp. 1295-1307.
[7] Belhumeur P. N., Hespanha J., Kriegman D. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. and Machine Intelli., 1997, 19(7):711-720.
[8] Jain A. K., Prabhakar S., Hong L., and Pankanti S. Filterbank-based fingerprint matching. IEEE Transactions on Image Processing, May 2000, vol.9, pp. 846-859.
[9] Jain A. K., Lin H., Bolle R. On-line fingerprint verification. IEEE Transaction on Pattern Analysis and Machine Intelligence, April 1997, Volume: 19 Issue: 4 , Page(s): 302 -314.
[10] Hong L., Wan Y, Jain A. K. Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, vol. 20, no. 8, pp. 777-789.
[11] Zuev Y, Ivanon S.The Voting As a Way to Increase the Decision Reliability. Foundations of Information/Decision Fusion with Applications to Engineering Problems, Washington, DC, USA. pp. 206-210.
[12] Cai J, Goshtasby A. Detecting Human Faces in Color Images. Image and Vision Computing, 1999,18(1): 63-75
[13] Yang G Z, Huang T S. Human Face Detection in a Complex Background. Pattern Recognition, 1994, 27(1): 53-63
[14] Brunelli R, Poggio T. Face Recognition: Features Versus Templates. IEEE Trans Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042-1052
[15] Rowley H A, Baluja S, Kanade T. Neural Network-based Face Detection. IEEE Trans Pattern Analysis and Machine Intelligence, 1998, 20(1): 23-38
[16] Samaria F, Young S. HMM based architecture for face recognition. Image and Computer Vision, 1994, 12(10): 537-543
[17] Nefian A V, Hayes Ⅲ M H. Hidden Markov models for face recognition. Proceedings of IEEE International Conference on Image Processing, Michigan Avenue Chicago, Illinois, USA, 1998, 141-145
[18] Mallat S. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation [J]. IEEE Trans. PAMI, 1989, 11(5): 674-693.
[19] Wire Sweldens. The Lifting Scheme: A New Philosophy in Biorthogonal Wavelet Constructions. Wavelet Applications in Signal and Image Processing Ⅱ [J], Proc. SPIE, 1995, 2569: 6879.
[20] Mallat S. Wavelet for vision [J], Proc. IEEE, 1996, 84(4), 605-614.
[21] Edgar E. Osuna, Freund R., Girosi F. Support Vector Machines: Training and Applications. AI Memo 1602, MIT Artificial Intelligence Laboratory, 1997. http://citeseernj.nec.com/osuna97support.html.
[22] Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[23] Bledsoe W. Man-machine Facial Recognition. Panoramic Research Inc, Palo Alto, CA, 1966, Rep PRI: 22.
[24] Turk M. A., Pentland A. P. Eigenfaces for Recognition, J. Cogn. Neurosci., 1991, vol. 3, no. 1, pp. 71-86.
[25] Berto R, Poggio T. Face recognition: Feature versus templates. IEEE Trans. on PAMI, 1993, 15 (10): 1042-1052.
[26] Ziquan Hong. Algebraic Feature Extraction of Image for Recognition. Pattern Recognition, 1991, 24 (3): 211-219.
[27] Nakamura O, Mathur S, Minami T. Identification of Human Faces Based on Isodensity Maps. Pattern Recognition, 1991, 24 (3): 263-272.
[28] Lades M, Vorbuggen J, Buhmann J, et al. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. on Computers, 1991, 42 (3): 300-311.
[29] Barrlet M.S., Movellan J.R., Sejnowski T.J. Face Recognition by Independent Component Analysis. IEEE Trans.on Neural Networks, 2002, Vol. 13, no.6, pp.1450-1464
[30] Keun-Chang Kwak, Witold Redrycz. Face Recognition Using an Enhanced Independent Component Analysis Approach. Submitted: Jun, 17, 2004, Preprint
[31] Kirby M, Sirovich L. Application of the Karhunen-Loeve Procedure for the Charaterization of Human Faces. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1990, Vol.12, No.1: 103-108
[32] Fisher R.A. The Use of Multiple Measures in Taxonomic Problems. Ann. Eugenics, 1936, Vol. 7:179-188
[33] 边肇祺，张学工．模式识别．清华大学出版社，2000．212-227
[34] Peter N. Belhumeur, Joao P. Hespanha, David J.kriefma. Eigenfaces vs. Fisheffaces, Recognition Using Class Specific Linear Projection. IEEE Transactions On Pattern Analysis And Machine Intelligence, July 1997, vol 19, no.7, pp. 711-720.
[35] Ratha N, Chen S, Jain A K. Adaptive Flow Orientation Based Feature Extraction in Fingerprint Images [J]. Pattern Recognition, Nov 1995, 28(11): 1657-1672.
[36] Mehtre B. Fingerprint Image Analysis for Automatic Identification [J]. Machine Vision and Application, 1993, 6(2-3): 124-139.
[37] Hong L, Jain A K, Bolle R, Pankanti. Identity Authentication Using Fingerprints [A]. Proc. Of First Int'l Conference on Audio and Video-Based Biometric Person Authentication[C], Geneva, Switzerland, 1997:103-110.
[38] Maio D, Maltioni D. Direct gray-scale minutiae detection in fingerprints [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(1): 27-40.
[39] Fang Xu-dong, Yau Wei-yun, Ser Wee. Detecting the Fingerprint Minutiae by Adaptive Tracing the Gray-level Ridge [J]. Pattern Recognition, 2001, 34(5): 999-1013.
[40] Lee Hc, Gaensslen R E. Advances in Fingerprint Technology [M]. New York: Elsevier, 1991.
[41] Jian Bing, Zhuang Zhenquan, et al. A Minutiae Extraction Algorithm Based on Ridgeline Flowing. Journal of Circuits and System, Sept 2001, Vol.6 No.3, pp: 1-5.
[42] Dario Maio. Direct Gray-Scale Minutiae Detection in Fingerprints [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 1(19): 27-40.
[43] Lawrence O' Gorman, Feffref V. Nickersom. An Approach to Fingerprint Filter Design. Pattern Recognition, 1989, 22(1): 29-38.
[44] Jain K, Prabhakar S, Lin H, et al. Filterbank-based Fingerprint Matching [J], IEEE Transactions on Image Processing, 2000, 9(5): 846-859.
[45] Kenneth Nilsson, Josef Bign: Localization of Corresponding Points in Fingerprints by Complex Filtering. Pattern Recognition Letters, 2003, 24(13): 2135-2144
[46] Daugman J.G. High Confidence Recognition of Persons by a Test of Statistical Independence. IEEE Trans. Pattern Anal. Machine Intell., 1993, vol. 15, no. 11, pp. 1148-1161.
[47] Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters, J. Opt. Soc. Amer. A, 1985, vol. 2, pp. 1160-1169.
[48] Jain A. K., Prabhakar S., Hong L. A Multichannel Approach to Fingerprint Classification. IEEE Trans. Pattern Anal. Machine Intell., 1999, vol. 21, no. 4, pp. 348-359.
[49] Robert W. Frischholz, Ulrich Deickmann. BioID: A Multimodal Biometric Identification System. IEEE Computer, February 2000, Vol. 33, No. 2.
[50] Hong L., Jain A.K., Panikanti S., Can Multibiometrics Improve Perfomance? Proceedings AutoID'99, Summit, NJ, October 1999, pp. 59-64.
[51] Sharath Panikanti, Ruud M. Bolle, Anil Jain. Biometrics: The Future of Identification. IEEE Computer, February 2000, Vol. 33, No. 2.
[52] Zuev Y., Ivanon S. The Voting as a Way to Increase the Decision Reliability. Foundations of Information/Decision Fusion with Applications to Engineering Problems, Washington, DC, USA. 1996, pp. 206-210.
[53] Kalyan Veeramachaneni, Lisa Osadciw, Pramod Varshney. An Adaptive Multimodal Biometric Management Algorithm. IEEE Trans, on Systems, Man, and Cybernetics: PART C: Applications and Reviews, August 2005, Vol. 35, No. 3.
[54] Lin Hong, Anil Jain. Integrating Faces and Fingerprints for Personal Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec, 1998, Vol. 20, No. 12, pp. 1295-1307.
[55] Salil Prabhakar, Anil Jain. Decision-level Fusion in Fingerprint Verification. Pattern Recognition, 2002, vol. 35, pp. 861-874.
[56] Steven M. Kay. Fundamentals of Statistical Signal Processing: Detection Theory, Vol. II, Prentice-Hall, Inc., 1998.
[57] Ramanarayanan Viswanathan, Pramod K. Varshney. Distributed Detection With Multiple Sensors: Part I - Fundamentals. Proceedings of the IEEE, Jan., 1997, Vol. 85, No. 1, pp. 54 - 63.
[58] Pramod K. Varshney. Distributed Detection and Data Fusion. New York, Springer, 1997.
[59] Kennedy J., Eberhart R. C, Shi Y. H. Swarm Intelligence. Morgan Kaufmann Publishers, 2001.
[60] Tillet J., Rao R., Sahin F., Rao T. M. Cluster-head Identification in Ad Hoc Sensor Networks Using Particle Swarm Optimization. Proceedings of the IEEE International Conference on Personal Wireless Communication, 2002.
[61] Rajani Muraleedharan, Lisa Ann Osadciw. Balancing The Performance of a Sensor Network Using an Ant System. Proceedings of CISS, Baltimore, Maryland, March 12-14, 2003.
[62] Eberhart R., Shi, Y. Comparison between Genetic Algorithms and Particle Swarm Optimization. The 7th Annual Conference on Evolutionary Programming, San Diego, USA, 1998.
[63] Kennedy J., Eberhart R. C. Particle swarm optimization. Proc. IEEE Int'l. Conf. on Neural Networks, Piscataway, NJ: IEEE Service Center, 1995, IV, 1942-1948.
[64] Afshinmanesh E, Marandi A., Rahimi-Kian A. A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System. Proc. of IEEE International Conference on Computer as a Tool (EUROCON 2005).
[65] Clerc M.; Kennedy J. The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, Feb. 2002, Volume 6, Issue 1, Page(s):58-73
[66] Liu H, Abraham A. Fuzzy Turbulent Particle Swarm Optimization. Proceeding of the 5th International Conference on Hybrid Intelligent Systems, Brazil, IEEE CS Press, USA, 2005.
[67] Pohlheim H. Genetic Algorithm Toolbox Test Functions. Department of Automatic Control and Systems Engineering University of Sheffield.
[68] Anthony Carlisle, Gerry Dozier. An Off-The-Shelf PSO. PSO Workshop IUPUI, Indianapolis, April, 2001.
[69] Shi Y., Eberhart R. C. Empirical Study of Particle Swarm Optimization. Congress on Evolutionary Computing, 1999, Vol III, pp 1945-1950.
[70] The ORL database is available from http://www.camorl.co.uk/facedatabase.html
[71] Graham D., Allinson N, 1998 [Online]. The UMIST database is available from: http://images.ee.umist.ac.uk/danny/database.html.
[72] Ortega-Garcia J., Fierrez-Aguilar J., Simon D., Gonzalez J., Faundez-Zanuy M., Espinosa V., Satue A., Hernaez I., Igarza J. J., Vivaracho C, Escudero D., Moro Q. I., MCYT Baseline Corpus: Abimodal Biometric Database. IEE Proc.-Vis. Image Signal Processing, December 2003, Vol. 150, No. 6.
[73] Artiklar M., Hassoun M., Watta, P. Application of a Postprocessing Algorithm for Improved Human Face Recognition. Proceedings of the IEEE International Conference on Neural Networks, Washington DC, July 10-16,1999, IJCNN-1999.
[74] Mu X., Artiklar M., Artiklar M., Hassoun M. H., Watta P. Training Algorithms for Robust Face Recognition Using a Template-matching Approach. Proceedings of the International Joint Conference on Neural Networks, 2001, v 4, p 2877-2882
[2] Ben-Yacoub S., Abdeljaoued Y., Mayoraz E. Fusion of Face and Speech Data for Person Identity Verification. Dalle Molle Institute for Perceptual Artificial Intelligence (IDIAPj, 1999.
[3] Souheil Ben-Yacoub, etc. Fusion of Face and Speech Data for Person Verification. IEEE Transaction on Neural Networks, 1999, vol. 10(5): 1065-1074.
[4] Ross A. and Jain A. K. Information Fusion in Biometrics, Pattern Recognition Letters, September, 2003, Vol. 24, Issue 13, pp. 2115-2125.
[5] Turk M. A., Pentland A. P. Eigenfaces for recognition, J. Cognitive Neurosci., 1991, vol. 3, no. 1, pp. 71-86.
[6] Hong L., Jain A. K. Integrating Faces and Fingerprints for Personal Identification. IEEETruns. on Portern Anal. and Machine Inrell., 1998, vol. 20, no. 12, pp. 1295-1307.
[7] Belhumeur P. N., Hespanha J., Kriegman D. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. and Machine Intelli., 1997, 19(7):711-720.
[8] Jain A. K., Prabhakar S., Hong L., and Pankanti S. Filterbank-based fingerprint matching. IEEE Transactions on Image Processing, May 2000, vol.9, pp. 846-859.
[9] Jain A. K., Lin H., Bolle R. On-line fingerprint verification. IEEE Transaction on Pattern Analysis and Machine Intelligence, April 1997, Volume: 19 Issue: 4 , Page(s): 302 -314.
[10] Hong L., Wan Y, Jain A. K. Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, vol. 20, no. 8, pp. 777-789.
[11] Zuev Y, Ivanon S.The Voting As a Way to Increase the Decision Reliability. Foundations of Information/Decision Fusion with Applications to Engineering Problems, Washington, DC, USA. pp. 206-210.
[12] Cai J, Goshtasby A. Detecting Human Faces in Color Images. Image and Vision Computing, 1999,18(1): 63-75
[13] Yang G Z, Huang T S. Human Face Detection in a Complex Background. Pattern Recognition, 1994, 27(1): 53-63
[14] Brunelli R, Poggio T. Face Recognition: Features Versus Templates. IEEE Trans Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042-1052
[15] Rowley H A, Baluja S, Kanade T. Neural Network-based Face Detection. IEEE Trans Pattern Analysis and Machine Intelligence, 1998, 20(1): 23-38
[16] Samaria F, Young S. HMM based architecture for face recognition. Image and Computer Vision, 1994, 12(10): 537-543
[17] Nefian A V, Hayes Ⅲ M H. Hidden Markov models for face recognition. Proceedings of IEEE International Conference on Image Processing, Michigan Avenue Chicago, Illinois, USA, 1998, 141-145
[18] Mallat S. A Theory for Multiresolution Signal Decomposition: the Wavelet Representation [J]. IEEE Trans. PAMI, 1989, 11(5): 674-693.
[19] Wire Sweldens. The Lifting Scheme: A New Philosophy in Biorthogonal Wavelet Constructions. Wavelet Applications in Signal and Image Processing Ⅱ [J], Proc. SPIE, 1995, 2569: 6879.
[20] Mallat S. Wavelet for vision [J], Proc. IEEE, 1996, 84(4), 605-614.
[21] Edgar E. Osuna, Freund R., Girosi F. Support Vector Machines: Training and Applications. AI Memo 1602, MIT Artificial Intelligence Laboratory, 1997. http://citeseernj.nec.com/osuna97support.html.
[22] Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[23] Bledsoe W. Man-machine Facial Recognition. Panoramic Research Inc, Palo Alto, CA, 1966, Rep PRI: 22.
[24] Turk M. A., Pentland A. P. Eigenfaces for Recognition, J. Cogn. Neurosci., 1991, vol. 3, no. 1, pp. 71-86.
[25] Berto R, Poggio T. Face recognition: Feature versus templates. IEEE Trans. on PAMI, 1993, 15 (10): 1042-1052.
[26] Ziquan Hong. Algebraic Feature Extraction of Image for Recognition. Pattern Recognition, 1991, 24 (3): 211-219.
[27] Nakamura O, Mathur S, Minami T. Identification of Human Faces Based on Isodensity Maps. Pattern Recognition, 1991, 24 (3): 263-272.
[28] Lades M, Vorbuggen J, Buhmann J, et al. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. on Computers, 1991, 42 (3): 300-311.
[29] Barrlet M.S., Movellan J.R., Sejnowski T.J. Face Recognition by Independent Component Analysis. IEEE Trans.on Neural Networks, 2002, Vol. 13, no.6, pp.1450-1464
[30] Keun-Chang Kwak, Witold Redrycz. Face Recognition Using an Enhanced Independent Component Analysis Approach. Submitted: Jun, 17, 2004, Preprint
[31] Kirby M, Sirovich L. Application of the Karhunen-Loeve Procedure for the Charaterization of Human Faces. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1990, Vol.12, No.1: 103-108
[32] Fisher R.A. The Use of Multiple Measures in Taxonomic Problems. Ann. Eugenics, 1936, Vol. 7:179-188
[33] 边肇祺，张学工．模式识别．清华大学出版社，2000．212-227
[34] Peter N. Belhumeur, Joao P. Hespanha, David J.kriefma. Eigenfaces vs. Fisheffaces, Recognition Using Class Specific Linear Projection. IEEE Transactions On Pattern Analysis And Machine Intelligence, July 1997, vol 19, no.7, pp. 711-720.
[35] Ratha N, Chen S, Jain A K. Adaptive Flow Orientation Based Feature Extraction in Fingerprint Images [J]. Pattern Recognition, Nov 1995, 28(11): 1657-1672.
[36] Mehtre B. Fingerprint Image Analysis for Automatic Identification [J]. Machine Vision and Application, 1993, 6(2-3): 124-139.
[37] Hong L, Jain A K, Bolle R, Pankanti. Identity Authentication Using Fingerprints [A]. Proc. Of First Int'l Conference on Audio and Video-Based Biometric Person Authentication[C], Geneva, Switzerland, 1997:103-110.
[38] Maio D, Maltioni D. Direct gray-scale minutiae detection in fingerprints [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(1): 27-40.
[39] Fang Xu-dong, Yau Wei-yun, Ser Wee. Detecting the Fingerprint Minutiae by Adaptive Tracing the Gray-level Ridge [J]. Pattern Recognition, 2001, 34(5): 999-1013.
[40] Lee Hc, Gaensslen R E. Advances in Fingerprint Technology [M]. New York: Elsevier, 1991.
[41] Jian Bing, Zhuang Zhenquan, et al. A Minutiae Extraction Algorithm Based on Ridgeline Flowing. Journal of Circuits and System, Sept 2001, Vol.6 No.3, pp: 1-5.
[42] Dario Maio. Direct Gray-Scale Minutiae Detection in Fingerprints [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 1(19): 27-40.
[43] Lawrence O' Gorman, Feffref V. Nickersom. An Approach to Fingerprint Filter Design. Pattern Recognition, 1989, 22(1): 29-38.
[44] Jain K, Prabhakar S, Lin H, et al. Filterbank-based Fingerprint Matching [J], IEEE Transactions on Image Processing, 2000, 9(5): 846-859.
[45] Kenneth Nilsson, Josef Bign: Localization of Corresponding Points in Fingerprints by Complex Filtering. Pattern Recognition Letters, 2003, 24(13): 2135-2144
[46] Daugman J.G. High Confidence Recognition of Persons by a Test of Statistical Independence. IEEE Trans. Pattern Anal. Machine Intell., 1993, vol. 15, no. 11, pp. 1148-1161.
[47] Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters, J. Opt. Soc. Amer. A, 1985, vol. 2, pp. 1160-1169.
[48] Jain A. K., Prabhakar S., Hong L. A Multichannel Approach to Fingerprint Classification. IEEE Trans. Pattern Anal. Machine Intell., 1999, vol. 21, no. 4, pp. 348-359.
[49] Robert W. Frischholz, Ulrich Deickmann. BioID: A Multimodal Biometric Identification System. IEEE Computer, February 2000, Vol. 33, No. 2.
[50] Hong L., Jain A.K., Panikanti S., Can Multibiometrics Improve Perfomance? Proceedings AutoID'99, Summit, NJ, October 1999, pp. 59-64.
[51] Sharath Panikanti, Ruud M. Bolle, Anil Jain. Biometrics: The Future of Identification. IEEE Computer, February 2000, Vol. 33, No. 2.
[52] Zuev Y., Ivanon S. The Voting as a Way to Increase the Decision Reliability. Foundations of Information/Decision Fusion with Applications to Engineering Problems, Washington, DC, USA. 1996, pp. 206-210.
[53] Kalyan Veeramachaneni, Lisa Osadciw, Pramod Varshney. An Adaptive Multimodal Biometric Management Algorithm. IEEE Trans, on Systems, Man, and Cybernetics: PART C: Applications and Reviews, August 2005, Vol. 35, No. 3.
[54] Lin Hong, Anil Jain. Integrating Faces and Fingerprints for Personal Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec, 1998, Vol. 20, No. 12, pp. 1295-1307.
[55] Salil Prabhakar, Anil Jain. Decision-level Fusion in Fingerprint Verification. Pattern Recognition, 2002, vol. 35, pp. 861-874.
[56] Steven M. Kay. Fundamentals of Statistical Signal Processing: Detection Theory, Vol. II, Prentice-Hall, Inc., 1998.
[57] Ramanarayanan Viswanathan, Pramod K. Varshney. Distributed Detection With Multiple Sensors: Part I - Fundamentals. Proceedings of the IEEE, Jan., 1997, Vol. 85, No. 1, pp. 54 - 63.
[58] Pramod K. Varshney. Distributed Detection and Data Fusion. New York, Springer, 1997.
[59] Kennedy J., Eberhart R. C, Shi Y. H. Swarm Intelligence. Morgan Kaufmann Publishers, 2001.
[60] Tillet J., Rao R., Sahin F., Rao T. M. Cluster-head Identification in Ad Hoc Sensor Networks Using Particle Swarm Optimization. Proceedings of the IEEE International Conference on Personal Wireless Communication, 2002.
[61] Rajani Muraleedharan, Lisa Ann Osadciw. Balancing The Performance of a Sensor Network Using an Ant System. Proceedings of CISS, Baltimore, Maryland, March 12-14, 2003.
[62] Eberhart R., Shi, Y. Comparison between Genetic Algorithms and Particle Swarm Optimization. The 7th Annual Conference on Evolutionary Programming, San Diego, USA, 1998.
[63] Kennedy J., Eberhart R. C. Particle swarm optimization. Proc. IEEE Int'l. Conf. on Neural Networks, Piscataway, NJ: IEEE Service Center, 1995, IV, 1942-1948.
[64] Afshinmanesh E, Marandi A., Rahimi-Kian A. A Novel Binary Particle Swarm Optimization Method Using Artificial Immune System. Proc. of IEEE International Conference on Computer as a Tool (EUROCON 2005).
[65] Clerc M.; Kennedy J. The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation, Feb. 2002, Volume 6, Issue 1, Page(s):58-73
[66] Liu H, Abraham A. Fuzzy Turbulent Particle Swarm Optimization. Proceeding of the 5th International Conference on Hybrid Intelligent Systems, Brazil, IEEE CS Press, USA, 2005.
[67] Pohlheim H. Genetic Algorithm Toolbox Test Functions. Department of Automatic Control and Systems Engineering University of Sheffield.
[68] Anthony Carlisle, Gerry Dozier. An Off-The-Shelf PSO. PSO Workshop IUPUI, Indianapolis, April, 2001.
[69] Shi Y., Eberhart R. C. Empirical Study of Particle Swarm Optimization. Congress on Evolutionary Computing, 1999, Vol III, pp 1945-1950.
[70] The ORL database is available from http://www.camorl.co.uk/facedatabase.html
[71] Graham D., Allinson N, 1998 [Online]. The UMIST database is available from: http://images.ee.umist.ac.uk/danny/database.html.
[72] Ortega-Garcia J., Fierrez-Aguilar J., Simon D., Gonzalez J., Faundez-Zanuy M., Espinosa V., Satue A., Hernaez I., Igarza J. J., Vivaracho C, Escudero D., Moro Q. I., MCYT Baseline Corpus: Abimodal Biometric Database. IEE Proc.-Vis. Image Signal Processing, December 2003, Vol. 150, No. 6.
[73] Artiklar M., Hassoun M., Watta, P. Application of a Postprocessing Algorithm for Improved Human Face Recognition. Proceedings of the IEEE International Conference on Neural Networks, Washington DC, July 10-16,1999, IJCNN-1999.
[74] Mu X., Artiklar M., Artiklar M., Hassoun M. H., Watta P. Training Algorithms for Robust Face Recognition Using a Template-matching Approach. Proceedings of the International Joint Conference on Neural Networks, 2001, v 4, p 2877-2882