Advancements in Facial Expression Recognition: A Comprehensive Analysis of Techniques

This review paper presents a comprehensive evaluation and comparative analysis of three models for facial expression recognition using the FER2013 dataset. The models under examination encompass traditional hand-crafted feature-based methods, state-of-the-art deep learning architectures, and innovative hybrid approaches. The paper traces the evolution of facial expression recognition techniques, from classical methods relying on SIFT, HOG, and LBP features to the emergence of convolutional neural networks (CNNs) in deep learning. Additionally, it explores hybrid methodologies that combine traditional and deep learning techniques to enhance performance. Detailed analyses of each model’s architecture, strengths, limitations, and performance on the FER2013 dataset are provided. For thorough evaluation, performance indicators like accuracy, precision, recall, and F1-score are used. The review concludes with insights into the significance of continuous research and future directions for advancing facial expression recognition, especially in the context of the FER2013 dataset. This publication provides important direction for researchers and practitioners in the field by offering significant knowledge on the state-of-the-art in facial emotion recognition.