Skin Feature

Skin feature tracking, focusing on accurately locating and following specific skin points across images or videos, is crucial for various applications including remote heart rate monitoring and assessing motor function in neurological disorders. Current research emphasizes the use of deep learning, particularly convolutional neural networks and autoencoders, to improve tracking accuracy and robustness compared to traditional methods like SIFT and Lucas-Kanade, even under challenging conditions such as significant motion or varying illumination. These advancements enable more reliable and efficient image analysis in medical imaging, virtual try-on technologies, and other fields requiring precise skin feature localization.