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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...
The task of the ChaLearn Apparent Personality Analysis: First Impressions Challenge is to rate/quantify personality traits of users in short video sequences. Although the validity of personality judgments from short interactions is questionable, studies show the possibility of predicting attributed traits (First Impressions) using facial [15] and acoustic [13] features. The challenge introduces a...
Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other...
Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries - signature forgeries that target a particular individual. In previous...
In this paper we present a novel audio-visual (AV) person identification system based on joint sparse representation. Video features used were vectorized raw pixel values, while i-vectors were used as the audio features. Classification is performed by solving the joint sparsity optimization problem, and fusion is carried out by using the quality (confidence) assigned to each matcher. Our experimental...
Visual inspection of bulk material is the thorough optical inspection of streams of granular material to assess their quality or to detect defective objects. Examples are found in mining (discovery of ores), recycling (sorting waste from reusable material) and food safety (detection of pathogens). In these applications, it is generally not feasible or even possible to provide an accurate and exhaustive...
Inferring the aesthetic quality of images is a challenging computer vision task due to its subjective and conceptual nature. Most image aesthetics evaluation approaches focused on designing handcrafted features, and only a few adopted learning of relevant and imperative characteristics in a data-driven manner. In this paper, we propose to attune Convolutional Neural Networks (CNNs) for image aesthetics...
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