A Comprehensive Review of Face Detection Techniques for Occluded Faces: Methods, Datasets, and Open Challenges

Abstract

Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks, sunglasses, and other obstructions. Addressing this issue is crucial for applications such as surveillance, biometric authentication, and human-computer interaction. This paper provides a comprehensive review of face detection techniques developed to handle occluded faces. Studies are categorized into four main approaches: feature-based, machine learning-based, deep learning-based, and hybrid methods. We analyzed state-of-the-art studies within each category, examining their methodologies, strengths, and limitations based on widely used benchmark datasets, highlighting their adaptability to partial and severe occlusions. The review also identifies key challenges, including dataset diversity, model generalization, and computational efficiency. Our findings reveal that deep learning methods dominate recent studies, benefiting from their ability to extract hierarchical features and handle complex occlusion patterns. More recently, researchers have increasingly explored Transformer-based architectures, such as Vision Transformer (ViT) and Swin Transformer, to further improve detection robustness under challenging occlusion scenarios. In addition, hybrid approaches, which aim to combine traditional and modern techniques, are emerging as a promising direction for improving robustness. This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world, occlusion-prone environments. Further improvements and the proposal of broader datasets are required to develop more scalable, robust, and efficient models that can handle complex occlusions in real-world scenarios