Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation - Flynn, Maria; Effraimidis, Dimitris; Angelopoulou, Anastassia; Kapetanios, Epaminondas; Williams, David; Hemanth, Jude; Towell, Tony (Frontiers in Human Neuroscience, 2020)
Recent success stories in automated object or face recognition, partly fuelled by deep learning artiﬁcial neural network (ANN) architectures, has led to the advancement of biometric research platforms and, to some extent, the resurrection of Artiﬁcial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have taken place to automate the recognition of emotions in adults or children for the beneﬁt of various applications such as identiﬁcation of children emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straight forward with several challengesarisingforbothscience(e.g., methodologyunderpinnedbypsychology) and technology (e.g., iMotions biometric research platform). In this paper, we present a methodology, experiment and interesting ﬁndings, which raise the following research questions for the recognition of emotions and attention in humans: a) adequacy of well-established techniques such as the International Affective Picture System (IAPS), b) adequacy of state-of-the-art biometric research platforms, c) the extent to which emotional responses may be different among children or adults. Our ﬁndings and ﬁrst attempts to answer some of these research questions, are all based on a mixed sample of adults and children, who took part in the experiment resulting into a statistical analysis of numerous variables. These are related with, both automatically and interactively, captured responses of participants to a sample of IAPS pictures.