In this paper, we show how to use audio to supervise the learning of active speaker detection in video. Voice Activity Detection (VAD) guides the learning of the vision-based classifier in a weakly supervised manner. The classifier uses spatio-temporal features to encode upper body motion - facial expressions and gesticulations associated with speaking. We further improve a generic model for active speaker detection by learning person specific models. Finally, we demonstrate the online adaptation of generic models learnt on one dataset, to previously unseen people in a new dataset, again using audio (VAD) for weak supervision. The use of temporal continuity overcomes the lack of clean training data. We are the first to present an active speaker detection system that learns on one audio-visual dataset and automatically adapts to speakers in a new dataset. This work can be seen as an example of how the availability of multi-modal data allows us to learn a model without the need for supervision, by transferring knowledge from one modality to another.
There has been some work on person specific facial expression recognition and transferring generic to specific models for improving classification performance @cite_9 @cite_16 @cite_5 . @cite_9 show that facial expression recognition results improve when using person specific classifiers. They use an Inductive Transfer Learning (ITL) approach, where they learn a source classifier, which is a collection of weak learners in a boosting framework. Subsequently a subset of these are used for training the target classifier with a small number of labeled target samples.
@cite_5 @cite_9 @cite_16
2090213288 2008635359
The way in which human beings express emotions depends on their specific personality and cultural background. As a consequence, person independent facial expression classifiers usually fail to accurately recognize emotions which vary between different individuals. On the other hand, training a person-specific classifier for each new user is a time consuming activity which involves collecting hundreds of labeled samples. In this paper we present a personalization approach in which only unlabeled target-specific data are required. The method is based on our previous paper [20] in which a regression framework is proposed to learn the relation between the user's specific sample distribution and the parameters of her his classifier. Once this relation is learned, a target classifier can be constructed using only the new user's sample distribution to transfer the personalized parameters. The novelty of this paper with respect to [20] is the introduction of a new method to represent the source sample distribution based on using only the Support Vectors of the source classifiers. Moreover, we present here a simplified regression framework which achieves the same or even slightly superior experimental results with respect to [20] but it is much easier to reproduce. Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. The alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+, GEMEP-FERA and RU-FACS. STM outperformed generic classifiers in all.