Real-Time Facial Emotion Recognition Using Deep Learning Approach

Classifying a person's emotional states is done using facial emotion recognition. The goal is to classify each face image into one of the 7 types of facial emotions: fear, disgust, surprise, sadness, neutral, happiness, and anger. To categorize the feeling CNN is utilized, and input is obtained via a variety of grayscale images via data collection and real-time videos. Subsequently, the CNN convolution and pooling layers are used for feature extraction, while the softmax layer is employed for categorization. Some techniques used to reduce the model's overfitting issue include dropout, cluster standardization, and L2 regularization. The model we developed outperforms previous efforts in accurately predicting individual emotions in the experiments conducted on the image collection of facial expressions. Additionally, the model performs well when used to forecast each image's sentiment using real-time video data. The developed deep learning model will collaborate with advancements in neuroscience, contributing to our understanding of the brain's mechanisms for emotion recognition. This may lead to more biologically inspired models and treatments for emotion-related disorders like autism.