No abstract

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. Figure 1: Sample frames of spontaneous neutral (odd rows) and painful (even rows) facial expressions taken from the PAINFUL dataset. The frames illustrate how human expressions like pain can be exhibited in many different ways, depending on the individual's appearance or personality.

Abstract-In the last decades, many efforts have been devoted to develop methods for automatic scene understanding in the context of video surveillance applications. This paper presents a novel non-object centric approach for complex scene analysis. Similarly to previous methods, we use low-level cues to individuate atomic activities and create clip histograms. Differently from recent works, the task of discovering high-level activity patterns is formulated as a convex prototype learning problem. This problem results into a simple linear program that can be solved efficiently with standard solvers. The main advantage of our approach is that, using as objective function the Earth Mover's Distance (EMD), the similarity among elementary activities is taken into account in the learning phase. To improve scalability we also consider some variants of EMD adopting L 1 as ground distance for one and two dimensional, linear and circular histograms. In these cases only the similarity between neighboring atomic activities, corresponding to adjacent histogram bins, is taken into account. Therefore we also propose an automatic strategy for sorting atomic activities. Experimental results on publicly available datasets show that our method compares favorably with state-of-the-art approaches, often outperforming them.

Understanding visual interestingness is a challenging task addressed by researchers in various disciplines ranging from humanities and psychology to, more recently, computer vision and multimedia. The rise of infographics and the visual information overload that we are facing today have given this task a crucial importance. Automatic systems are increasingly needed to help users navigate through the growing amount of visual information available, either on the web or our personal devices, for instance by selecting relevant and interesting content. Previous studies indicate that visual interest is highly related to concepts like arousal, unusualness, or complexity, where these connections are found based on psychological theories, user studies, or computational approaches. However, the link between visual interestingness and other related concepts has been only partially explored so far, for example, by considering only a limited subset of covariates at a time. In this article, we present a comprehensive survey on visual interestingness and related concepts, aiming to bring together works based on different approaches, highlighting controversies, and identifying links that have not been fully investigated yet. Finally, we present some open questions that may be addressed in future works. Our work aims to support researchers interested in visual interestingness and related subjective or abstract concepts, providing an in-depth overlook at state-of-the-art theories in humanities and methods in computational approaches, as well as providing an extended list of datasets.

Facial expression and gesture recognition algorithms are key enabling technologies for Human-Computer Interaction (HCI) systems. State of the art approaches for automatic detection of body movements and for analyzing emotions from facial features heavily rely on advanced machine learning algorithms. Most of these methods are designed for the average user, but the assumption "one-size-fits-all" ignores diversity in cultural background, gender, ethnicity and personal behaviour and limits their applicability in real world scenarios. A possible solution is to build personalized interfaces, which practically implies learning person-specific classifiers and usually collecting a significant amount of labeled samples for each novel user. As data annotation is a tedious and time consuming process, in this paper we present a framework for personalizing classification models which does not require labeled target data. Personalization is achieved by devising a novel transfer learning approach. Specifically, we propose a regression framework which exploits auxiliary (source) annotated data to learn the relation between person-specific sample distributions and the parameters of the corresponding classifiers. Then, when considering a new target user, the classification model is computed by simply feeding the associated (unlabeled) sample distribution into the learned regression function. We evaluate the proposed approach in different applications: pain recognition and action unit detection using visual data and gestures classification using inertial measurements, demonstrating the generality of our method with respect to different input data types and basic classifiers. We also show the advantages of our approach in terms of accuracy and computational time both with respect to user-independent approaches and to previous personalization techniques.