TOWARDS IMPROVING MATCHING IN BIOMETRIC SYSTEMS

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MOUTAFIS-DISSERTATION-2015.pdf (2.53 MB)

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2015-12

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Abstract

The integration of biometric technologies with authentication systems allows us to distinguish individuals easier, faster, and more accurately. As a result, biometric authentication is becoming increasingly important for various applications such as access control and financial transactions. However, despite the encouraging results obtained in controlled environments, biometric authentication remains a challenging problem in real-life conditions. Regardless of whether a biometric system relies on face, fingerprint, or any other biometric trait, it must perform (i) template matching to generate similarity scores that reflect the degree of similarity of the biometric samples matched and (ii) score-level processing to generate improved similarity scores. Depending on the biometric modality used, different challenges arise that degrade the recognition performance including: (i) distortions due to the different data acquisition conditions, (ii) artifacts introduced by pre-processing algorithms, (iii) incomplete utilization of the available information, and (iv) having to match data from different views. To address these challenges, we have developed new matching algorithms and score-processing methods that increase the recognition performance of biometric systems irrespective of the biometric trait used. Specifically, our contributions include: (i) a method that learns a non-linear distance metric for matching templates from the same view, (ii) a method that maps data from different views to a common discriminant space using non-linear projections, (iii) a score normalization framework that fully utilizes multiple samples per gallery subject, gallery-based information, and past experiences, and (iv) a score normalization framework for multimodal score fusion.

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Biometrics, Recognition, Score Normalization, Score Fusion, Distance Metric Learning, Cross Domain Matching, Multi-view Matching

Citation

Portions of this document appear in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Exploiting Score Distributions for Biometric Applications." In Face Recognition Across the Imaging Spectrum, pp. 333-353. Springer, Cham, 2016. And in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Can we do better in unimodal biometric systems? A rank-based score normalization framework." IEEE transactions on cybernetics 45, no. 12 (2014): 2654-2667. And in: Leng, Mengjun, Panagiotis Moutafis, and Ioannis A. Kakadiaris. "Joint prototype and metric learning for set-to-set matching: Application to biometrics." In 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1-8. IEEE, 2015. And in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Rank-based score normalization for multi-biometric score fusion." In 2015 IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1-6. IEEE, 2015. And in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Semi-coupled basis and distance metric learning for cross-domain matching: Application to low-resolution face recognition." In IEEE international joint conference on biometrics, pp. 1-8. IEEE, 2014. And in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Towards intelligent decision making for risk screening." In Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-2. 2014. And in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Gs4: Generating synthetic samples for semi-supervised nearest neighbor classification." In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 393-403. Springer, Cham, 2014. And in: Moutafis, Panagiotis, and Ioannis A. Kakadiaris. "Can we do better in unimodal biometric systems? A novel rank-based score normalization framework for multi-sample galleries." In 2013 International Conference on Biometrics (ICB), pp. 1-8. IEEE, 2013.

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https://hdl.handle.net/10657/7877

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