Contactless Human Emotion Analysis Across Different Modalities

Emotion recognition and analysis is an essential part of affective computing which plays a vital role nowadays in healthcare, security systems, education, etc. Numerous scientific researches have been conducted developing various types of strategies, utilizing methods in different areas to identify human emotions automatically. Different types of emotions are distinguished through the combination of data from facial expressions, speech, and gestures. Also, physiological signals, e.g., EEG (Electroencephalogram), EMG (Electromyogram), EOG (Electrooculogram), blood volume pulse, etc. provide information on emotions. The main idea of this paper is to identify various emotion recognition techniques and denote relevant benchmark data sets and specify algorithms with state-of-the-art results. We have also given a review of multimodal emotion analysis, which deals with various fusion techniques of the available emotion recognition modalities. The results of the existing literature show that emotion recognition works best and gives satisfactory accuracy if it uses multiple modalities in context. At last, a survey of the rest of the problems, challenges, and corresponding openings in this field is given.