Emotions are multimodal processes that play a crucial role in our everyday lives. Recognizing emotions is becoming more critical in a wide range of application domains such as healthcare, education, human-computer interaction, Virtual Reality, intelligent agents, entertainment, and more. Facial macro-expressions or intense facial expressions are the most common modalities in recognizing emotional states. However, since facial expressions can be voluntarily controlled, they may not accurately represent emotional states. Earlier studies have shown that facial micro-expressions are more reliable than facial (...) macro-expressions for revealing emotions. They are subtle, involuntary movements responding to external stimuli that cannot be controlled. This paper proposes using facial micro-expressions combined with brain and physiological signals to more reliably detect underlying emotions. We describe our models for measuring arousal and valence levels from a combination of facial micro-expressions, Electroencephalography (EEG) signals, galvanic skin responses (GSR), and Photoplethysmography (PPG) signals. We then evaluate our model using the DEAP dataset and our own dataset based on a subject-independent approach. Lastly, we discuss our results, the limitations of our work, and how these limitations could be overcome. We also discuss future directions for using facial micro-expressions and physiological signals in emotion recognition. (shrink)

COVID-19 has uncovered the vulnerabilities, inequalities and fragility present within our social community which has exposed and exacerbated the pre-existing racial and socioeconomic inequalities that disproportionately affect health outcomes for Black, Asian and Minority Ethnic (BAME) people. Such disparities are fuelled by complex socioeconomic health determinants and longstanding structural inequalities. This paper aims to explore the inequalities and vulnerabilities of BAME communities laid bare by the Public Health England (PHE) reports published in June 2020, concluding with suggested strategies to address (...) inequalities in a post COVID-19 recovery. (shrink)