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In this paper, we propose an automated, data-driven and unobtrusive framework to analyze interactional synchrony. We use this information to determine whether interpersonal synchrony can be an indicator of deceit. Our framework includes a robust facial tracking module, an effective expression recognition method, synchrony feature extraction and feature selection methods. These synchrony features are...
Recent years have witnessed a growing interest in the sparse representation problem. Prior work demonstrated that adaptive dictionary learning techniques can greatly improve the performance of sparse representation approaches. Existing techniques mainly focus on the reconstructive accuracies and the discriminative power of the learned dictionary, whereas the mutual incoherence between any two basis...
Semi-supervised learning is important when labeled data are scarce. In this paper, we develop a novel semi-supervised spectral feature selection technique using label regression and by using l\-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for semi-supervised feature selection. In the first step, we use label propagation and label...
We propose a method to detect the inconsistency between a subject and the speaker for extracting speech scenes from news videos. Speech scenes in news videos contain a wealth of multimedia information, and are valuable as archived material. In order to extract speech scenes from news videos, there is an approach that uses the position and size of a face region. However, it is difficult to extract...
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