CONF Chingovska_CVPRWORKSHOPONBIOMETRICS_2013/IDIAP Anti-spoofing in action: joint operation with a verification system Chingovska, Ivana Anjos, André Marcel, Sébastien biometric recognition Counter-Measures Fusion Spoofing trustworthy vulnerability EXTERNAL https://publications.idiap.ch/attachments/papers/2013/Chingovska_CVPRWORKSHOPONBIOMETRICS_2013.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Chingovska_Idiap-RR-19-2013 Related documents Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Biometrics Portland, Oregon 2013 Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses and attacks. However, spoofing counter-measures are not designated to operate stand-alone, but as a part of a recognition system they will protect. In this paper, we study techniques for decision-level and score-level fusion to integrate a recognition and anti-spoofing systems, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently. By doing so, we are able to report the impact of different spoofing counter-measures, fusion techniques and thresholding on the overall performance of the final recognition system. For a specific use-case covering face verification, experiments show to what extent simple fusion improves the trustworthiness of the system when exposed to spoofing attacks. REPORT Chingovska_Idiap-RR-19-2013/IDIAP Anti-spoofing in action: joint operation with a verification system Chingovska, Ivana Anjos, André Marcel, Sébastien Anti-spoofing Counter-Measures recognition security verification EXTERNAL https://publications.idiap.ch/attachments/reports/2013/Chingovska_Idiap-RR-19-2013.pdf PUBLIC Idiap-RR-19-2013 2013 Idiap May 2013 Besides the recognition task, today's biometric systems need to cope with additional problem: spoofing attacks. Up to date, academic research considers spoofing as a binary classification problem: systems are trained to discriminate between real accesses and attacks. However, spoofing counter-measures are not designated to operate stand-alone, but as a part of a recognition system they will protect. In this paper, we study techniques for decision-level and score-level fusion to integrate a recognition and anti-spoofing systems, using an open-source framework that handles the ternary classification problem (clients, impostors and attacks) transparently. By doing so, we are able to report the impact of different counter-measures, fusion techniques and thresholding on the overall performance of the final recognition system. For a specific use-case covering face verification, experiments show to what extent simple fusion improves the trustworthiness of the system when exposed to spoofing attacks.