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Multimodal recognition of affective states is a difficult problem, unless the recording conditions are carefully controlled. For recognition “in the wild”, large variances in face pose and illumination, cluttered backgrounds, occlusions, audio and video noise, as well as issues with subtle cues of expression are some of the issues to target. In this paper, we describe a multimodal approach for video-based...
We describe an end-to-end system for explainable automatic job candidate screening from video CVs. In this application, audio, face and scene features are first computed from an input video CV, using rich feature sets. These multiple modalities are fed into modality-specific regressors to predict apparent personality traits and a variable that predicts whether the subject will be invited to the interview...
Affective computing, particularly emotion and personality trait recognition, is of increasing interest in many research disciplines. The interplay of emotion and personality shows itself in the first impression left on other people. Moreover, the ambient information, e.g. the environment and objects surrounding the subject, also affect these impressions. In this work, we employ pre-trained Deep Convolutional...
We propose a two-level system for apparent age estimation from facial images. Our system first classifies samples into overlapping age groups. Within each group, the apparent age is estimated with local regressors, whose outputs are then fused for the final estimate. We use a deformable parts model based face detector, and features from a pretrained deep convolutional network. Kernel extreme learning...
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