%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Chingovska_CVPRWORKSHOPONBIOMETRICS_2013,
         author = {Chingovska, Ivana and Anjos, Andr{\'{e}} and Marcel, S{\'{e}}bastien},
       keywords = {biometric recognition, Counter-Measures, Fusion, Spoofing, trustworthy, vulnerability},
       projects = {Idiap, TABULA RASA, BEAT},
          month = jun,
          title = {Anti-spoofing in action: joint operation with a verification system},
      booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Biometrics},
           year = {2013},
       location = {Portland, Oregon},
       crossref = {Chingovska_Idiap-RR-19-2013},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2013/Chingovska_CVPRWORKSHOPONBIOMETRICS_2013.pdf}
}



crossreferenced publications: 
@TECHREPORT{Chingovska_Idiap-RR-19-2013,
         author = {Chingovska, Ivana and Anjos, Andr{\'{e}} and Marcel, S{\'{e}}bastien},
       keywords = {Anti-spoofing, Counter-Measures, recognition, security, verification},
       projects = {Idiap},
          month = {5},
          title = {Anti-spoofing in action: joint operation with a verification system},
           type = {Idiap-RR},
         number = {Idiap-RR-19-2013},
           year = {2013},
    institution = {Idiap},
       abstract = {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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Chingovska_Idiap-RR-19-2013.pdf}
}