Emotion Unleashed: Real-Time FER in Video via Advanced Deep Learning Models

While conversing, showing emotions through facial expressions comes naturally to human beings. Face expression detection is tackled by computer vision to serve psychology, human-computer interaction, and security applications. In this work, we present the software we have designed and implemented to read the emotions on people’s faces. The system allows users to discern emotions on people’s faces using artificial intelligence. It is a challenging task that has piqued the interest of scholars worldwide in facial expression analysis in recent years. Regarding some technical aspects of the proposed method, we rely on Deep learning methods, which have proved robust in various scenarios, including facial emotion recognition. This paper introduces a Deep Learning-based approach that relies on a Convolutional Neural Network (CNN) to infer knowledge from facial images and distinguish features. These features are then used to assign a label to an emotion. Deep Learning algorithms, such as DeepFace, NASNet Mobile, EfficientNet V2, and Inception V2, are investigated on FER tasks. By analyzing data from the FER-13 and Face databases, our model has learned to recognize emotions.