Exploring Face Detection and Feature Extraction Strategies in Facial Expression Recognition: A Comprehensive Review

This paper explores the implementation of a facial expression recognition (FER) system using deep learning techniques and data augmentation. It delves into the development of a Facial Detection Convolutional Neural Network (FD-CNN) and a Self-correction Network (SCN) to address uncertainties and noisy labels in large-scale facial expression recognition systems. The study evaluates the effectiveness of various datasets and accuracy levels related to facial expression recognition, highlighting the potential applications in computer vision research areas such as human-computer interaction, security, animation, and emotional recognition. The paper also discusses the integration of feature extraction techniques, including data augmentation and deep learning features, to refine the learning parameters of the CNN models for optimal performance.