Abstract. During face-to-face communication, people continuously exchange para-linguistic information such as their emotional state through facial expressions, posture shifts, gaze patterns and prosody. These affective signals are subtle and complex. In this paper, we propose to explicitly model the interaction between the high level perceptual features using Latent-Dynamic Conditional Random Fields. This approach has the advantage of explicitly learning the sub-structure of the affective signals as well as the extrinsic dynamic between emotional labels. We evaluate our approach on the Audio-Visual Emotion Challenge (AVEC 2011) dataset. By using visual features easily computable using off-theshelf sensing software (vertical and horizontal eye gaze, head tilt and smile intensity), we show that our approach based on LDCRF model outperforms previously published baselines for all four affective dimensions. By integrating audio features, our approach also outperforms the audio-visual baseline.
In this paper we present a system for multi-pose face detection. Our system presents three main contributions. First, we introduce the use of asymmetric Haar features. Asymmetric Haar features provide a rich feature space, which allows to build classifiers that are accurate and much simpler than those obtained with other features. The second contribution is the use of a genetic algorithm to search efficiently in the extremely large parameter space of potential features. Using this genetic algorithm, we generate a feature set that allows to exploit the expressive advantage of asymmetric Haar features and is small enough to permit exhaustive evaluation. The third contribution is the application of a skin color-segmentation scheme to reduce the search space. Our system uses specialized detectors in different face poses that are built using AdaBoost and the C4.5 rule induction algorithm. Experimental results using the CMU profile test set and BioID frontal faces test set, in addition to our own multi-pose face test set, show that our system is competitive with other systems presented recently in the literature.
We present a system for object detection applied to street detection in satellite images. Our system is based on asymmetric Haar features. Asymmetric Haar features provide a rich feature space, which allows to build classifiers that are accurate and much simpler than those obtained with other features. The extremely large parameter space of potential features is explored using a genetic algorithm. Our system uses specialized detectors in different street orientations that are built using AdaBoost and the C4.5 rule induction algorithm. Experimental results show that Asymmetric Haar features are better than basic Haar features for street detection.
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