Facial Emotion Recognition: The Future of Understanding Human Interaction

Emotions are critical features in effective communication. The process of expressing and detecting emotional states enhances interpersonal interactions and bolsters strong relationships that are unique to human behavior. Read on to find out about how facial emotion recognition uses AI to understand human interaction.

In transcending ethical differences and cultural barriers, emotions serve as a universal language in regular conversation. While some signals are more reliable and genuine than others, emotional expression can frequently convey an individual’s mental state by nonverbally reflecting his or her intentions (Mehta et al., 2018).

How does AI apply to facial emotion recognition

Given the rising prominence of artificial intelligence and computing in everyday life, simulation of natural interaction has become particularly valued in computer-based applications.

By accurately interpreting emotional states, machines have the potential to provide objective psychological analyses in various fields. Efforts are mainly directed toward the detection and interpretation of human emotions for the purposes of imitation and response.

Some aspects of facial emotion recognition remain challenging, as an individual may intentionally or inadvertently attempt to disguise his or her emotions with misleading expressions. Interpreting another’s intentions based on visible facial expressions alone may be detrimental and lead to a conflictual relationship.

In these cases, to reveal an individual’s genuine emotional state, micro-expressions should be considered. Unlike their “macro” counterparts, micro-expressions refer to rapid, involuntary muscular movements.

Since they are difficult to prevent or manipulate, micro-expressions are particularly expressive in discerning repressed emotions. While micro-expressions are extremely difficult for the naked eye to discern, developments in computer vision techniques have reported promising advances (Xia, et al., 2020).

Recently, convolutional neural networks (CNNs) have been implemented to extract and interpret both overt and elusive facial expressions. This use of deep learning for image classification problems has yielded efficient and accurate detection of facial emotions, though some aspects of conventional frameworks can be optimized (Minaee & Abdolrashidi, 2019).

What is Human Machine Interaction?

Computing provides machines with the emotional tools to carry out socially intelligent communication, or human-machine interaction (HMI).

With respect to the subfield of human-robot interaction (HRI), robots are trained to possess and exhibit human-like characteristics on three levels: first, in their emotional state; second, in their outward expression; and third, in their ability to infer human emotional state.

This third aspect utilizes facial emotion recognition techniques alongside analysis of thermal changes in facial images, body language and kinematics, brain activity, voice, and peripheral physiological responses.

The ability to interpret a human user’s expressions is crucial in gauging the situation and responding in an adequate manner (Spezialetti et al., 2020). In combination with body language interpretation and classification of tone of voice, facial emotional recognition is a powerful field that has the potential to revolutionize the field of human-machine interaction.

References

Mehta, D., Siddiqui, M. F. H., & Javaid, A. Y. (2018). Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors (Basel), 18(2), 416. https://doi.org/10.3390/s18020416

Minaee, S., & Abdolrashidi, A. (2019). Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network. CoRR, abs/1902.0. http://arxiv.org/abs/1902.01019

Spezialetti, M., Placidi, G., & Rossi, S. (2020). Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives. Frontiers in Robotics and AI, 7, 145. https://doi.org/10.3389/frobt.2020.532279