Research on face specular removal and intrinsic decomposition based on polarization characteristics

It is well known that the specular component in the face image destroys the true informantion of the original image and is detrimental to the feature extraction and subsequent processing. However, in many face image processing tasks based on Deep Learning methods, the lack of effective datasets and methods has led researchers to routinely neglect the specular removal process. To solve this problem, we formed the first high-resolution Asian Face Specular-Diffuse-Image-Material (FaceSDIM) dataset based on polarization characterisitics, which consists of real human face specular images, diffuse images, and various corresponding material maps. Secondly, we proposed a joint specular removal and intrinsic decomposition multi-task GAN to generate a de-specular image, normal map, albedo map, residue map and visibility map from a single face image, and also further verified that the prediected de-specular images have a positive enhancement effect on face intrinsic decomposition. Compared with the SOTA algorithm, our method achieves optimal performance both in corrected linear images and in uncorrected wild images of faces.