Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.Keywords Explainable computer vision · First impressions · Personality analysis · Multimodal information · Algorithmic accountability 1 IntroductionLooking at People (LaP) -the field of research focused on the visual analysis of human behavior -has been a very active research field within computer vision in the last decade [28,29,62]. Initially, LaP focused on tasks associated with basic human behaviors that were obviously visual (e.g., basic gesture recognition [71,70] or face recognition in restricted scenarios [10,83]). Research progress in LaP has now led to models that can solve those initial tasks relatively easily [66,82]. Instead, attention on human behavior analysis has now turned to problems that are not visually evident to model / recognize [84,48,72]. For instance, consider the task of assessing personality traits from visual information [72]. Although there are methods that can estimate apparent personality traits with (relatively) acceptable performance, model recommendations by themselves are useless if the end user is not confident on the model's reasoning, as the primary use for such estimation is to understand bias in human assessors.Explainability and interpretability are thus critical features of decision support systems in some LaP tasks [26]. The former focuses on mechanisms that can tell what is the rationale behind the decision or recommendation made by

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International audienceThis paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration

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In this paper, we propose a new model to simulate the movement of virtual humans based on trajectories captured automatically from filmed video sequences. These trajectories are grouped into similar classes using an unsupervised clustering algorithm, and an extrapolated velocity field is generated for each class. A physically-based simulator is then used to animate virtual humans, aiming to reproduce the trajectories fed to the algorithm and at the same time avoiding collisions with other agents. The proposed approach provides an automatic way to reproduce the motion of real people in a virtual environment, allowing the user to change the number of simulated agents while keeping the same goals observed in the filmed video.

Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.