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Great breakthrough has been made on Classic 2-D face recognition in normal environment. The facenet [1] proposed by Google in 2015 has 99.65% accuracy on LFW dataset. It can be considered that the performance of traditional 2-D network in normalized environment such as security check and bank is reliable enough. However, the accuracy of 2-D face recognition will significantly reduce when processing angular faces or occluded face. This paper claims that the lack of depth information is the reason why the recognition ability of neural network is inferior to that of human beings under complex conditions. Furthermore, we have proposed a method, which can obtain 100% accuracy on both Texas and Bosphorus dataset by using both 3-D deepmap and 2-D rgb picture as input.

Mixnet Face Recognition How Combing 2D and 3D Data Can Increase the Precision