Gloss assessment with deep photometric stereo: application to human skin

Abstract

This contribution presents a novel method to extract skin physical parameters as geometry, colour and gloss with photometric stereo. Our method is based on QNN (Quaternion Neural Network) to estimate the surface geometry from images with a fixed viewpoint modifying surface illumination, i.e. photometric stereo. To that end, we assume that surface BRDF (Bidirectional Reflectance Distribution Function) can be separated by a diffuse and specular component. Once the geometry is estimated, colour is estimated from geometry to finally compute gloss. This method results on multiple gloss maps which are used to compute features that characterise surface gloss. Unlike other approaches, our method does not require polarising filters that suffer from a more complex light modelling. We demonstrate the effectiveness of our approach through experiments on rendering, cow leather and ex-vivo skin samples. The proposed method has potential for various real-world applications such as evaluating the appearance of skin care products or assessing skin health.