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References
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24. M. Brady, Biometric recognition using a classi cation neural network, U.S. patent, 1999. 25. G. K. Venayagamoorthy, V. Moonasar, and K. Sandrasegaran, Voice Recognition Using Neural Networks, in Proceedings of the 1998 South African Symposium on Communications and Signal Processing, 1998, COMSIG 98, Rondebosch, South Africa, 7 8 September 1998, pp. 29 32. 26. A. K., Jain, A., Ross, and S., Prabhakar, An introduction to biometric recognition, IEEE Trans. Circuits Sys. Video Techno. 14(1):4 20, 2004.
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Designing Classi ers for Fusion-Based Biometric Veri cation
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Kritha Venkataramani and B. V. K. Vijaya Kumar
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INTRODUCTION
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The natural variability of biometric features presents challenges to recognition. In some practical situations, there may be a large variability present in the intra-person features and perhaps small differences between the inter-person features. Pose, illumination, and occlusion present major challenges in face recognition. For example, in the Face Recognition Grand Challenge (FRGC) [1] Experiment 4, involving the matching of studio-quality gallery images to probe images corresponding to uncontrolled illumination, the baseline principal component analysis (PCA) method has a correct veri cation rate of only 12% at 0.1% false accept rate (FAR). Challenges in ngerprint recognition are due to (a) elastic distortion caused by applying pressure on the nger on a sensor surface and (b) varying environmental conditions such as dryness, moisture, dirt, and so on, present in ngers. Varying eyelid occlusion in iris images causes dif culty in iris recognition. To mitigate the effect of such impairments, multiple sources of information/experts/classi ers can be fused to improve accuracy. Dasarathy [2] provides different classi cations of fusion based on the application, objective, input output characteristics, and sensor-suite con guration. Among the input output characteristics, classi cation is based on data, features, or classi er outputs. Multimodal fusion (e.g., fusion of face and nger) and multisensor fusion (e.g., visual and infrared camera data) are examples of data fusion. Sensor-suite
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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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4
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Designing Classi ers for Fusion-Based Biometric Veri cation
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con gurations fall into serial or parallel sensor fusion. This chapter focuses on parallel classi er output fusion for biometric veri cation. Kuncheva [3] reviews different classi er fusion strategies. Classi er decisions or scores can be fused. In addition to combining outputs from all classi ers, classi er selection can be done depending on the accuracy of the classi ers on the test sample. The most common decision fusion examples are Majority voting and weighted-Majority voting [3]. Other decision fusion methods such as Naive Bayes [4] and Behavior Knowledge Space [5] estimate the posterior probability of the decision vector. Examples of papers on score fusion are references 6 and 7. These are mainly empirical in nature, where the error rates of simple fusion rules such as the sum or the product of scores are compared on databases to nd the best fusion rule. There is some theoretical analysis as to when fusion accuracy can be improved for linear and order-statistic score combiners [8] and Majority voting [3, 9]. Ho et al. [10] introduced the concept of dynamic classi er selection (DCS) as an alternative to classi er ensemble combination where the most appropriate classi er is chosen to make the decision. Classi er selection is typically done by estimating the local accuracy (around the test point) of the classi ers in the test phase. This is attempted by nding the K nearest neighbors to the test input in the training or validation set and then computing the competence of the classi ers on these K objects [11]. There are several variations of this approach [12, 13]. The classi er ensemble generation/selection is much more important than the selection or fusion. This is because none of these methods are effective in reducing the ensemble fusion error when the classi er ensemble has poor diversity. There is a lack of theory on generating classi er ensembles that have the desired statistical dependence on their outputs. Some methods have been attempted to generate classi er ensembles that have desirable statistical dependence for sum score fusion and Majority fusion [14], but have not been successful. There are some classi er ensemble selection strategies that select diverse classi ers from among randomly generated classi ers [15 17]. Such selection strategies are suboptimal in general, and fail to take into account the complete statistical dependence between all classi er outputs. This chapter provides guidelines for optimal ensemble generation, where each classi er in the ensemble is of the same type (base classi er). This approach is applicable to most base classi ers. Examples are shown here for support vector machines [18] and correlation lters [19]. While background and references for different base classi ers are not provided here, the readers are referred to other sources for such information [3, 20]. Decision fusion rules are focused on in this chapter since the space of decision N fusion rules is large and xed. For N classi ers, there are 22 decision fusion rules. There is some similarity between common decision fusion rules such as the Majority rule and typical score fusion rules such as the Sum rule. Hence, some of the ideas presented here are applicable to score fusion too. Section 4.2 analyzes the effect of classi er output diversity on their decision fusion accuracy. It is found that the Or, And, and Majority decision rules are important because of the likeliness of one of them being the best fusion rule when the individual classi ers have the same accuracy. Section 4.3 analyzes the Or rule fusion in detail. The diversity between
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