000150613 001__ 150613
000150613 005__ 20180913055849.0
000150613 037__ $$aREP_WORK
000150613 245__ $$aIntroducing Crossmodal Biometrics: Person Identification from Distinct Audio & Visual Streams
000150613 269__ $$a2010
000150613 260__ $$bIdiap$$c2010
000150613 336__ $$aReports
000150613 520__ $$aPerson identification using audio or visual biometrics is a well-studied problem in pattern recognition. In this scenario, both training and testing are done on the same modalities. However, there can be situations where this condition is not valid, i.e. training and testing has to be done on different modalities. This could arise, for example, in covert surveillance. Is there any person specific information common to both the audio and visual (video-only) modalities which could be exploited to identify a person in such a constrained situation? In this work, we investigate this question in a principled way and propose a framework which can perform this task consistently better than chance, suggesting that such crossmodal biometric information exists.
000150613 700__ $$0243369$$aRoy, Anindya$$g177638
000150613 700__ $$0243994$$aMarcel, Sébastien$$g143942
000150613 8564_ $$uhttp://publications.idiap.ch/downloads/reports/2010/Roy_Idiap-RR-29-2010.pdf$$zURL
000150613 8564_ $$s480056$$uhttps://infoscience.epfl.ch/record/150613/files/Roy_Idiap-RR-29-2010.pdf$$zn/a
000150613 909C0 $$0252189$$pLIDIAP$$xU10381
000150613 909CO $$ooai:infoscience.tind.io:150613$$pSTI$$preport
000150613 937__ $$aEPFL-REPORT-150613
000150613 970__ $$aRoy_Idiap-RR-29-2010/LIDIAP
000150613 973__ $$aEPFL$$sPUBLISHED
000150613 980__ $$aREPORT