Face Detection

Face detection, the automated identification of human faces in images and videos, aims to accurately locate and delineate facial features for various applications. Current research emphasizes developing lightweight and efficient models, such as those based on MobileNetV2 and RetinaFace architectures, to improve speed and reduce computational demands, particularly for resource-constrained devices. Furthermore, research addresses challenges like detecting faces in low-resolution images, handling occlusions (e.g., masks), and mitigating biases in existing algorithms to ensure fairness and robustness across diverse populations. These advancements have significant implications for numerous fields, including security, healthcare, human-computer interaction, and autonomous systems.