Face Occlusion

Face occlusion, the obstruction of facial features in images, presents a significant challenge for computer vision tasks like facial recognition and emotion detection. Current research focuses on developing robust algorithms, often employing deep learning architectures such as convolutional neural networks (CNNs) and vision transformers, to mitigate the impact of occlusions from various sources (e.g., masks, glasses, hands). These efforts aim to improve the accuracy and reliability of face-related applications in diverse settings, including security systems, human-computer interaction, and medical diagnosis, where occlusions are common. The development of large, high-quality datasets with diverse occlusion types is also a key area of focus, enabling the training and evaluation of more effective models.