Anti-spoofing in action: joint operation with a verification system
| Type of publication: | Conference paper |
| Citation: | Chingovska_CVPRWORKSHOPONBIOMETRICS_2013 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Biometrics |
| Year: | 2013 |
| Month: | June |
| 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. |
| Keywords: | biometric recognition, Counter-Measures, Fusion, Spoofing, trustworthy, vulnerability |
| Projects: |
Idiap TABULA RASA BEAT |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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