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42. J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, Face recognition using kernel direct discriminant analysis algorithms, IEEE Trans. Neural Networks, 14(1):117 126, 2003. 43. T. K. Moon and W. C. Stirling, Mathematical methods and Algorithms for Signal Processing, Prentice-Hall, Upper Saddle River, NJ, 2000. 44. Q. Liu, X. Tang, H. Lu, and S. Ma, Face recognition using kernel scatter-difference-based discriminant analysis, IEEE Trans. Neural Networks 17(4):1081 1085, 2006. 45. Z. Liang, High-dimensional discriminant analysis and its application to color face images, in Proceedings of the International Conference on Pattern Recognition, Vol. 2, August 2006, pp. 917 920. 46. D. Tao, X. Li, X. Wu, and S. J. Maybank, Human carrying status in visual surveillance, in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 2006, pp. 1670 1677. 47. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, Uncorrelated multilinear discriminant analysis with regularization and aggregation for tensor object recognition, IEEE Trans. Neural Networks, 2009, to appear. 48. T. Sim, S. Baker, and M. Bsat, The CMU pose, illumination, and expression database, IEEE Trans. Pattern Anal. Mach. Intell. 25(12):1615 1618, 2003. 49. A. Georghiades, P. Belhumeur, and D. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Machine Intell. 23(6):643 660, 2001. 50. K. C. Lee, J. Ho, and D. Kriegman, Acquiring linear subspaces for face recognition under variable lighting, IEEE Trans. Pattern Anal. Mach. Intell., 27(5):684 698, 2005. 51. J. Ye, Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems, J. Mach. Learning Res. 6:483 502, 2005. 52. D. Cai, X. He, J. Han, and H. J. Zhang, Orthogonal laplacianfaces for face recognition, IEEE Trans. Image Processing 15(11):3608 3614, 2006. 53. J. D. Carroll and J. J. Chang, Analysis of individual differences in multidimensional scaling via an n-way generalization of eckart-young decomposition, Psychometrika 35:283 319, 1970. 54. R. A. Harshman, Foundations of the parafac procedure: Models and conditions for an explanatory multi-modal factor analysis, in UCLA Working Papers in Phonetics, Vol. 16, 1970, pp. 1 84. 55. L. R. Tucker, Some mathematical notes on three-mode factor analysis, Psychometrika, 31:279 311, 1966.
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A Comparative Survey on Biometric Identity Authentication Techniques Based on Neural Networks
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INTRODUCTION
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Biometrics is the study of methods for uniquely identifying or authenticating humans based on intrinsic physical or behavioral traits. Identi cation means characteristics are selected from a database, to produce a list of possible or likely matches. Authentication means that when a person makes a claim that he or she is that speci c person, just that speci c person s characteristics are being checked to see if they match. The two important operations in a biometric system are enrollment and test. During enrollment the biometric of the individual is stored as a database, and during test the biometric information of the individual is detected and compared with the stored database. Various biometric techniques are currently used, as shown in Figure 3.1. In this chapter, we select representative works on neural networks that describe biometric-based methodologies on voice, iris, nger, palm, and face. We do not cover all the work done in the eld of neural networks (NN)-based biometrics, but we select a small set of NN-based methods from different forms of biometrics in order to capture the evolution of some of the most representative neural networks based methods. The objective is to provide to the readers the general idea behind NN-based biometrics and their future [1 26].
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Biometrics: Theory, Methods, and Applications. Edited by Boulgouris, Plataniotis, and Micheli-Tzanakou Copyright 2010 the Institute of Electrical and Electronics Engineers, Inc.
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A Comparative Survey on Biometric Identity Authentication Techniques
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Figure 3.1. Examples of biometric characteristics [26].
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The overall organization of the chapter consists of seven main sections that re ect on the grouping of the methodologies into clusters of the same or similar biometric. Each of the rst seven sections provides a brief description of the methodologies presented in the selected works and their advantages and disadvantages. In the eighth section, we provide a comparative study of the selected methodologies in order to show the status of their current performance and the potential improvement to their maximum level. Finally, Section 3.9 presents the conclusion of this study.
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