Information fusion and decision cascading for audio-visual speaker recognition based on time-varying stream reliability prediction

We examine the techniques for multi-modal biometric information fusion for verification and identification of speakers, where the reliability of each data stream, either audio of video, is modeled with parameters that are time-varying and depend on the context created by its local behavior. The complementary nature and the time dependent relative reliability of audio and video data is studied in the context of verification and identification, on data collected during a user's interaction with an automated system. Of significance is that this data is not corrupted artificially. Particular focus is directed to verification and its ability to refine identification decisions, by indicating a level of confidence in the system decisions. Results show more striking effects for verification, when using time-dependent fusion, than for identification.