3D Face Recognition using Photometric Stereo and Deep Learning
Bryan Kneis, Wenhao Zhang · 2020
Illumination variance is one of the largest real-world problems when deploying face recognition systems. Over the last few years much work has gone into the development of novel 3D face recognition methods to overcome this issue. Photometric stereo is a well-established 3D reconstruction technique capable of recovering the normals and albedo of a surface. Although it provides a way to obtain 3D data, the amount of training data available captured using photometric stereo often does not provide sufficient modelling capacity for training state-of-the-art feature extractors, such as deep convolutional neural networks, from scratch.