Facial Expression Recognition: A Survey of Techniques, Datasets, and Real-World Challenges

Abstract

Facial expressions are a powerful nonverbal communication tool that can convey emotions, thoughts, and intentions, enhancing the richness and effectiveness of human interaction. Facial Expression Recognition (FER) has gained increasing attention due to its applications in education, healthcare, marketing, and security. In this survey, we examine the key techniques and approaches employed in FER, focusing on three main categories: traditional machine learning, deep learning, and hybrid methods. We review traditional pipelines involving image preprocessing, feature extraction, and classification, along with deep learning methods such as convolutional neural networks (CNNs), transfer learning, attention mechanisms, and optimized loss functions. Furthermore, the study provides a comprehensive examination of existing research and available datasets related to emotion recognition. We also summarize the best-performing methods used with the most common datasets. In addition, the survey addresses the technical challenges of emotion recognition in real-world scenarios, such as variations in illumination, occlusion, and population diversity. The survey highlights state-of-the-art FER models, comparing their accuracy, efficiency, and limitations. Ultimately, this work serves as a comprehensive starting point for researchers, offering insights into current FER trends and guiding the development of more robust and accurate recognition systems.