Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Speaker-following Video Subtitles

We propose a new method for improving the presentation of subtitles in video (e.g. TV and movies). With conventional subtitles, the viewer has to constantly look away from the main viewing area to read the subtitles at the bottom of the screen, which disrupts the viewing experience and causes unnecessary eyestrain. Our method places on-screen subtitles next to the respective speakers to allow the viewer to follow the visual content while simultaneously reading the subtitles. We use novel identification algorithms to detect the speakers based on audio and visual information. Then the placement of the subtitles is determined using global optimization. A comprehensive usability study indicated that our subtitle placement method outperformed both conventional fixed-position subtitling and another previous dynamic subtitling method in terms of enhancing the overall viewing experience and reducing eyestrain

Audio-Visual Speaker Identification using the CUAVE Database

The freely available nature of the CUAVE database allows it to provide a valuable platform to form benchmarks and compare research. This paper shows that the CUAVE database can successfully be used to test speaker identifications systems, with performance comparable to existing systems implemented on other databases. Additionally, this research shows that the optimal configuration for decisionfusion of an audio-visual speaker identification system relies heavily on the video modality in all but clean speech conditions