Likelihood ratio based features for a trained biometric score fusion |
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| Authors: | Loris Nanni Alessandra Lumini Sheryl Brahnam |
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| Affiliation: | 1. School of Computer Science, Wuhan University, Wuhan, Hubei 430072, PR China;2. College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, PR China;3. School of Computer, Pingdingshan University, Pingdingshan, Henan 467000, PR China;4. School of Computer and Information Engineering, Henan University, Kaifeng, Henan 475001, PR China;5. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, PR China;1. Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Commerce, Changsha 410205, China;2. Key Laboratory of Hunan Province for New Retail Virtual Reality Technology, Hunan University of Commerce, Changsha 410205, China;3. School of Information Science and Engineering, Central South University, Changsha 410083, China |
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| Abstract: | In this work, we present a novel trained method for combining biometric matchers at the score level. The new method is based on a combination of machine learning classifiers trained using the match scores from different biometric approaches as features. The parameters of a finite Gaussian mixture model are used for modelling the genuine and impostor score densities during the fusion step.Several tests on different biometric verification systems (related to fingerprints, palms, fingers, hand geometry and faces) show that the new method outperforms other trained and non-trained approaches for combining biometric matchers.We have tested some different classifiers, support vector machines, AdaBoost of neural networks, and their random subspace versions, demonstrating that the choice for the proposed method is the Random Subspace of AdaBoost. |
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