000192368 001__ 192368
000192368 005__ 20181122043200.0
000192368 037__ $$aREP_WORK
000192368 088__ $$aIdiap-RR-19-2013
000192368 245__ $$aAnti-spoofing in action: joint operation with a verification system
000192368 269__ $$a2013
000192368 260__ $$bIdiap$$c2013
000192368 336__ $$aReports
000192368 520__ $$aBesides 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.
000192368 6531_ $$aAnti-spoofing
000192368 6531_ $$aCounter-Measures
000192368 6531_ $$arecognition
000192368 6531_ $$asecurity
000192368 6531_ $$averification
000192368 700__ $$0246047$$aChingovska, Ivana$$g211402
000192368 700__ $$aAnjos, André
000192368 700__ $$0243994$$aMarcel, Sébastien$$g143942
000192368 8564_ $$s11575281$$uhttps://infoscience.epfl.ch/record/192368/files/Chingovska_Idiap-RR-19-2013.pdf$$yn/a$$zn/a
000192368 909C0 $$0252189$$pLIDIAP$$xU10381
000192368 909CO $$ooai:infoscience.tind.io:192368$$pSTI$$pGLOBAL_SET$$preport
000192368 937__ $$aEPFL-REPORT-192368
000192368 970__ $$aChingovska_Idiap-RR-19-2013/LIDIAP
000192368 980__ $$aREPORT