Visual lip activity detection and speaker detection using mouth region intensities
IEEE Transactions on Circuits and Systems for Video Technology
Efficient video coding based on audio-visual focus of attention
Journal of Visual Communication and Image Representation
Finding audio-visual events in informal social gatherings
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
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A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an Mi-based objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdfs) of the features, nor for the cost function itself. The pdfs are estimated from the samples using a nonparametric approach. The challenging optimization problem is solved using a global method: the differential evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech production is discussed. Using these specific audio features, candidate video features are then classified as member of the "speaker" or "non-speaker" class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100% on in-house test sequences, and of 85% on most commonly used sequences.