Emotion Recognition Through Facial Expressions from Images Using Deep Learning Techniques

Emotion recognition is the ability to identify and understand the emotional states of others based on their facial expressions, body language, tone of voice, and other nonverbal cues. It is an important research topic in computer vision and has a wide range of applications. Emotion recognition from facial expressions is a challenging task in computer vision, and deep learning has shown remarkable performance in this area. In this study, we present a system for emotion recognition from facial expressions that utilizes YuNet Face Detection and Deep Learning. The YuNet face detection model is used for efficient and precise face detection, followed by the Deep Learning Model for emotion recognition. The proposed system is tested on the KDEF and CK+ datasets and compared with state-of-the-art methods. In this paper, we achieved an accuracy of 91.07% for the KDEF Dataset and 90.77% for CK+ Datasets. The results indicate that the proposed method yields high accuracy in emotion recognition while using significantly fewer parameters and computation resources than other methods. Furthermore, we test the proposed system on real-world images and videos to demonstrate its efficacy in recognizing emotions in diverse environments.