Semi-automatic Labeling and Training Strategy for Deep Learning-based Facial Wrinkle Detection

Facial wrinkle is very important in measuring aging. Image processing-based methods have been proposed for wrinkle detection, but their performance was not enough because wrinkles have a wide variety of thickness, shape, orientation, and vague boundaries. Recently, deep learning-based methods have been widely applied in the field of image recognition with a lot of labeled image dataset. To extend this technology to facial wrinkle detection, labeling work for wrinkles to generate ground truth is very important. However, it is difficult to label wrinkles accurately because of the wide variety. In this paper, we propose a semiautomatic labeling strategy incorporating a texture map and a deep learning model. Specifically, the proposed method extracted the texture map from an original image and removed non-wrinkle textures on the map by multiplying with a roughly labeled wrinkle mask. Then, the map is converted into ground truth by thresholding. Using the ground truth, a deep learning model was trained with the original image and the texture map. The trained model was evaluated with facial images obtained from real skin diagnosis devices, and the results showed superior performance to those of existing image processing-based methods.