Dense facial landmark mesh for facial skin movement understanding - Sinha, Jyona (Nanyang Technological University, 2023)
This study aims to understand facial skin movement using facial videos of individuals performing four extreme expressions, along with cutometer measurements taken for different skin parameters. Facial landmark mesh is employed to identify specific points around the cheek area where cutometer is used to measure skin elasticity. Skin Elasticity helps in understanding the facial skin movement, provides insights into the aging process and how fine lines change into wrinkles. Skin elasticity and other skin parameters are usually measured with a device called cutometer. This study explores the possibility of using computer vision techniques to predict facial skin elasticity by employing various machine learning and deep learning methods. Images of subjects captured from expression videos and their corresponding cutometer measurements are used for the experiments. The study comprises of subjects from different age groups and ethnicity to make the dataset more diverse. The findings of the research compare various models and highlight the improvements made to the model. The results show that it is possible to predict facial skin elasticity with accuracy using computer vision methods. Overall, this report sheds light on the potential use of computer vision techniques for predicting facial skin elasticity and offers valuable insights for future research. Researchers may be able to develop more accurate models and gain a deeper understanding of the relationship between skin movement and elasticity by expanding the dataset and exploring different techniques. This could lead to improved skincare products and treatments, as well as a better understanding of the aging process.