Computer Vision and Human Behaviour to Recognize Emotions

Computer vision denotes the study and application of methods and techniques that enable one or more computers to observe and understand the content of videos/images or the content of multidimensional data in real-world scenarios. Therefore, general psychology and computer vision are closely linked as they could help investigate the behaviour of human beings and the mental processes that characterise emotions and corresponding future actions. Emotion recognition in psychology refers to the attribution of emotional states based on the observation of visual and auditory non-verbal cues. Non-verbal cues include facial, vocal, postural and gestural indications shown by a sender, i.e. a person manifesting an emotional reaction. Emotions are expressed when interacting and socializing with other people. Studying how to read them can be a tough task, so computer vision can is used to do that task. Hence, the ability to recognize human emotions based on physiology and facial expressions open up important research and application opportunities, mainly in healthcare and human-computer interaction.

Nowadays, emotion recognition is a popular area of study in artificial intelligence, specifically in the computer vision domain. Thanks to novel technologies provided by machines and deep learning processes, machines can assume human-like attitudes while experiencing feelings. A multidisciplinary approach to computing, combining skills and knowledge from areas as diverse as engineering, neuroscience and behavioural psychology, can make a solid contribution to interpreting the emotional state of human beings and, consequently, be able to adapt its behaviour to its interactors, giving an appropriate response to those emotions. The research aims to find Computer Vision solutions suitable for recognising and managing emotions.

Authors can make available their latest research contributions in the multidisciplinary field of engineering, neuroscience and behavioural psychology applied to the field of computer vision. Therefore, contributions from all of the above areas are welcome. Areas of particular interest include, but are not limited to:

- Big Data Analytics;
- Artificial Intelligence;
- Computer Modelling & Applications;
- Data Mining;
- Information & Knowledge Engineering;
- Computer Vision & Virtual Reality;
- Augmented and Virtual Reality Computer-aided Design/Manufacturing
- Diagnosis in Healthcare;
- Digital Signal and Image Processing;
- Human-Computer Interaction;
- Pattern Recognition & Signal Analysis;
- Decision support systems;
- Distributed control systems;
- Expert systems for psychology;
- Hybrid learning systems;

Well-designed empirical works and studies, theoretical results highlighting insights generated by analyses performed, and successful examples of transferring knowledge from theoretical analyses or empirical studies into a practical scenario or theory inspired by phenomena observed in practice are welcome.