Recognition of Human Emotions Through The Use of Facial Expressions

Emotions play a pivotal role in human life, influencing communication, relationships, and productivity. Recognizing and understanding these emotions is crucial, and various methods have been explored, including monitoring brain activity. However, traditional approaches, such as electroencephalography (EEG), are costly and time-consuming, often yielding accuracy rates ranging from 31% to 81%. In this study, we propose an alternative approach: accurately detecting human emotions through facial expressions. Leveraging an existing simulator and neural networks, we achieved remarkable accuracy rates of up to 96%. We present the methodology, including image processing, differential readings, and facial expression detection, and compare our approach to other methods. The study sheds light on the potential of neural network-based systems for high-accuracy emotion recognition from facial expressions, offering a promising avenue for human-computer interaction and intelligent surveillance systems.