The advance of AI & and ML technology has opened new paths in acquiring resources. Emotion detection from facial expressions has a wide range of applications, from enhancing virtual communication to mental health assessments. This abstract provides an overview of a deep-learning approach for facial emotion detection. The proposed deep-learning model processes facial images and employs a combination of Convolutional Neural Network (CNNs) and Recurrent Neural Network (RNNs) to produce the information. The important steps in the process include preprocessing, feature extraction, model training, and evaluation. This model is trained to recognize a range of facial expressions such as happiness, sadness, anger, and fear. Additionally, the model can be further developed for specific applications, such as detecting stress or depression in facial expressions.

Deep Learning, Convolutional Neural Network (CNN), Recurrent Neural Network(RNN).