Abstract: Affective computing is presently one of the large amount dynamic research domains which include speech descriptors, Facial affect detection, multimodal system and Emotion classification. Affective computing is a multidisciplinary knowledge, such as psychology, cognitive and computer sciences. In this article, describe the overview of affective computing emotion theories, state of art of key technologies, projects, research challenges, emotion feature extraction, algorithms and applications. This paper analyzes the various emotions of humans such as happy, sad, anger, surprise and neutral emotions based on Warsaw set of facial emotional dataset. This research work comprises of two phases. The first phase contains the preprocessing of given image data. The preprocessing is comprised of grey scale conversion, histogram equalization, face detection algorithm and image cropping & resizing. The second stage is classification of emotions is based on Quantum Convolutional neural network. Our proposed work attain the recognition accuracy is 95%. The performance measures of the research work have been evaluated. The performance measures are recall, precision, and confusion matrix.

Emotion classification, Automated Face Analysis, Deep learning, Quantum Computing