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Face recognition from RGB-D images utilizes 2 complementary types of image data, i.e. colour and depth images, to achieve more accurate recognition. In this paper, we propose a face recognition system based on deep learning, which can be used to verify and identify a subject from the colour and depth face images captured with a consumer-level RGB-D camera. To recognize faces with colour and depth information, our system contains 3 parts: depth image recovery, deep learning for feature extraction, and joint classification. To alleviate the problem of the limited size of available RGB-D data for deep learning, our deep network is firstly trained with colour face dataset, and later fine-tuned on depth face images for transfer learning. Our experiments on some public and our own RGB-D face datasets show that the proposed face recognition system provides very accurate face recognition results and it is robust against variations in head rotation and environmental illumination.

Accurate and robust face recognition from RGB-D images with a deep learning approach