Abstract: The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends.

Abstract: This book contains the proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), endorsed by the International Association for Pattern Recognition (IAPR), and in cooperation with the ACM Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH), the European Association for Computer Graphics (EUROGRAPHICS), the EUROGRAPHICS Portuguese Chapter, the VRVis Center for Virtual Reality and Visualization Forschungs-GmbH, the French Association for Computer Graphics (AFIG), and the Society for Imaging Science and Technology (IS&T). The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthy of being disseminated to the interested research audiences. VISIGRAPP 2021 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions. This year VISIGRAPP was, exceptionally, held as a web-based event, due to the COVID-19 pandemic, from 8 – 10 February. We received a high number of paper submissions for this edition of VISIGRAPP, 371 in total, with contributions from 52 countries. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a hierarchical process of double-blind evaluation where each paper was reviewed by two to six experts from the International Program Committee (IPC). The IPC selected for oral presentation and for publication as full papers 12 papers from GRAPP, 8 from HUCAPP, 11 papers from IVAPP, and 56 papers from VISAPP, which led to a result for the full-paper acceptance ratio of 24% and a high-quality program. Apart from the above full papers, the conference program also features 118 short papers and 67 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, SCOPUS, DBLP, Semantic Scholar, Google Scholar, EI and Microsoft Academic, will help the Computer Vision, Imaging, Visualization, Computer Graphics and Human-Computer Interaction communities to find interesting research work. Moreover, we are proud to inform that the program also includes three plenary keynote lectures, given by internationally distinguished researchers, namely Federico Tombari (Google and Technical University of Munich, Germany), Dieter Schmalstieg (Graz University of Technology, Austria) and Nathalie Henry Riche (Microsoft Research, United States), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields. Furthermore, a short list of the presented papers will be selected to be extended into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2021 in the CCIS series. Moreover, a short list of presented papers will be selected for publication of extended and revised versions in a special issue of the Springer Nature Computer Science journal. All papers presented at this conference will be available at the SCITEPRESS Digital Library. Three awards are delivered at the closing session, to recognize the best conference paper, the best student paper and the best poster for each of the four conferences. There is also an award for best industrial paper to be delivered at the closing session for VISAPP. We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made it possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Finally, we gratefully acknowledge the professional support of the INSTICC team for all organizational processes, especially given the need to introduce online streaming, forum management, direct messaging facilitation and other web-based activities in order to make it possible for VISIGRAPP 2021 authors to present their work and share ideas with colleagues in spite of the logistic difficulties caused by the current pandemic situation. We wish you all an exciting conference. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org.

Abstract: Eating habits are learned throughout the early stages of our lives. However, it is not easy to be aware of how our food-related routine affects our healthy living. In this work, we address the unsupervised discovery of nutritional habits from egocentric photo-streams. We build a food-related behavioral pattern discovery model, which discloses nutritional routines from the activities performed throughout the days. To do so, we rely on Dynamic-Time-Warping for the evaluation of similarity among the collected days. Within this framework, we present a simple, but robust and fast novel classification pipeline that outperforms the state-of-the-art on food-related image classification with a weighted accuracy and F-score of 70% and 63%, respectively. Later, we identify days composed of nutritional activities that do not describe the habits of the person as anomalies in the daily life of the user with the Isolation Forest method. Furthermore, we show an application for the identification of food-related scenes when the camera wearer eats in isolation. Results have shown the good performance of the proposed model and its relevance to visualize the nutritional habits of individuals.

Abstract: Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

Abstract: Personality perception is implicitly biased due to many subjective factors, such as cultural, social, contextual, gender and appearance. Approaches developed for automatic personality perception are not expected to predict the real personality of the target, but the personality external observers attributed to it. Hence, they have to deal with human bias, inherently transferred to the training data. However, bias analysis in personality computing is an almost unexplored area. In this work, we study different possible sources of bias affecting personality perception, including emotions from facial expressions, attractiveness, age, gender, and ethnicity, as well as their influence on prediction ability for apparent personality estimation. To this end, we propose a multi-modal deep neural network that combines raw audio and visual information alongside predictions of attribute-specific models to regress apparent personality. We also analyse spatio-temporal aggregation schemes and the effect of different time intervals on first impressions. We base our study on the ChaLearn First Impressions dataset, consisting of one-person conversational videos. Our model shows state-of-the-art results regressing apparent personality based on the Big-Five model. Furthermore, given the interpretability nature of our network design, we provide an incremental analysis on the impact of each possible source of bias on final network predictions.

Abstract: Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase.

Abstract: Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.

Abstract: In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images.