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Child behaviour is a topic of great scientific interest across a wide range of disciplines, including social sciences and artificial intelligence (AI). Knowledge in these different fields is not yet integrated to its full potential. The aim of this workshop was to bring researchers from these fields together. The first two workshops had a significant impact. In this workshop, we discussed topics such as the use of AI techniques to better examine and model interactions and children’s emotional development, analyzing head movement patterns with respect to child age. This workshop was a successful new step towards the objective of bridging social sciences and AI, attracting contributions from various academic fields on child behaviour analysis. This document summarizes the accepted papers.

Bipolar disorder is a mental disorder that causes periods of manic and depressive episodes. In this work, we classify recordings from Bipolar Disorder corpus that contain 7 different tasks, into hypomania, mania, and remission classes using only speech features. We perform our experiments on splitted tasks from the interviews. Best results achieved on the model trained with 6th and 7th tasks together gives 0.53 UAR (unweighted average recall) result which is higher than the baseline results of the corpus., Comment: Conference, 5 pages, in Turkish language

Child behavior is a topic of great scientific interest across a wide range of disciplines, including social and behavioral sciences, as well as artificial intelligence (AI). The first workshop had a significant impact, and in this workshop, we aimed to bring together researchers from these fields to discuss topics such as using AI to better understand and model child behavioral and developmental processes, challenges and opportunities for AI in large-scale child behavior analysis, and implementing explainable ML/AI on sensitive child data. The workshop was a successful second step toward this objective, attracting contributions from many academic fields on child behavior analysis. This document summarizes the workshop’s events as well as the accepted papers and abstracts.

Classifying facial expressions is a vital part of developing systems capable of aptly interacting with users. In this field, the use of deep-learning models has become the standard. However, the inner workings of these models are unintelligible, which is an important issue when deploying them to high-stakes environments. Recent efforts to generate explanations for emotion classification systems have been focused on this type of models. In this work, an alternative way of explaining the decisions of a more conventional model based on geometric features is presented. We develop a geometric-features-based deep neural network (DNN) and a convolutional neural network (CNN). Ensuring a sufficient level of predictive accuracy, we analyze explainability using both objective quantitative criteria and a user study. Results indicate that the fidelity and accuracy scores of the explanations approximate the DNN well. From the performed user study, it becomes clear that the explanations increase the understanding of the DNN and that they are preferred over the explanations for the CNN, which are more commonly used. All scripts used in the study are publicly available.

Automated classification of animal vocalisations is a potentially powerful wildlife monitoring tool. Training robust classifiers requires sizable annotated datasets, which are not easily recorded in the wild. To circumvent this problem, we recorded four primate species under semi-natural conditions in a wildlife sanctuary in Cameroon with the objective to train a classifier capable of detecting species in the wild. Here, we introduce the collected dataset, describe our approach and initial results of classifier development. To increase the efficiency of the annotation process, we condensed the recordings with an energy/change based automatic vocalisation detection. Segmenting the annotated chunks into training, validation and test sets, initial results reveal up to 82% unweighted average recall (UAR) test set performance in four-class primate species classification., Comment: 5 pages, 3 figures, 2 tables

While both speech emotion recognition and music emotion recognition have been studied extensively in different communities, little research went into the recognition of emotion from mixed audio sources, i.e. when both speech and music are present. However, many application scenarios require models that are able to extract emotions from mixed audio sources, such as television content. This paper studies how mixed audio affects both speech and music emotion recognition using a random forest and deep neural network model, and investigates if blind source separation of the mixed signal beforehand is beneficial. We created a mixed audio dataset, with 25% speech-music overlap without contextual relationship between the two. We show that specialized models for speech-only or music-only audio were able to achieve merely 'chance-level' performance on mixed audio. For speech, above chance-level performance was achieved when trained on raw mixed audio, but optimal performance was achieved with audio blind source separated beforehand. Music emotion recognition models on mixed audio achieve performance approaching or even surpassing performance on music-only audio, with and without blind source separation. Our results are important for estimating emotion from real-world data, where individual speech and music tracks are often not available.

As emotions play a central role in human communication, automatic emotion recognition has attracted increasing attention in the last two decades. While multimodal systems enjoy high performances on lab-controlled data, they are still far from providing ecological validity on non-lab-controlled, namely “in-the-wild” data. This work investigates audiovisual deep learning approaches to emotion recognition in in-the-wild problem. Inspired by the outstanding performance of end-to-end and transfer learning techniques, we explored the effectiveness of architectures in which a modality-specific Convolutional Neural Network (CNN) is followed by a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) using the AffWild2 dataset under the Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol. We deployed unimodal end-to-end and transfer learning approaches within a multimodal fusion system, which generated final predictions using a weighted score fusion scheme. Exploiting the proposed deep-learning-based multimodal system, we reached a test set challenge performance measure of 48.1% on the ABAW 2020 Facial Expressions challenge, which advances the first-runner-up performance.

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, the evaluation protocol, 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., Comment: Preprint submitted to TAC

A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mixture of factor analyzers, carrying out simultaneous clustering and locally linear, globally nonlinear dimensionality reduction. Permitting different number of factors per mixture component, the algorithm adapts the model complexity to the data complexity. We compare the proposed algorithm with related automatic model selection algorithms on a number of benchmarks. The results indicate the effectiveness of this fast and robust approach in clustering, manifold learning and class-conditional modeling., Comment: Pre-print has 30 pages including the appendix and references. A MATLAB tool of the proposed method is available (see the conclusions section)