Techniques for facial affective computing: A review

Abstract

Facial affective computing has gained popularity and become a progressive research area, as it plays a key role in human-computer interaction. However, many researchers lack the right technique to carry out a reliable facial affective computing effectively. To address this issue, we presented a review of the state-of-the-art artificial intelligence techniques that are being used for facial affective computing. Three research questions were answered by studying and analysing related papers collected from some well-established scientific databases based on some exclusion and inclusion criteria. The result presented the common artificial intelligence approaches for face detection, face recognition and emotion detection. The paper finds out that the haar-cascade algorithm has outperformed all the algorithms that have been used for face detection, the Convolutional Neural Network (CNN) based algorithms have performed best in face recognition, and the neural network algorithm with multiple layers has the best performance in emotion detection. A limitation of this research is the access to some research papers, as some documents require a high subscription cost. Practice implication: The paper provides a comprehensive and unbiased analysis of existing literature, identifying knowledge gaps and future research direction and supports evidence-based decision-making. We considered articles and conference papers from well-established databases. The method presents a novel scope for facial affective computing and provides decision support for researchers when selecting plans for facial affective computing.