4D Unconstrained Real-time Face Recognition Using a Commodity Depthh Camera

Robust unconstrained real-time face recognition still remains a challenge today. The recent addition to the market of lightweight commodity depth sensors brings new possibilities for human-machine interaction and therefore face recognition. This article accompanies the reader through a succinct survey of the current literature on face recognition in general and 3D face recognition using depth sensors in particular. Consequent to the assessment of experiments performed using implementations of the most established algorithms, it can be concluded that the majority are biased towards qualitative performance and are lacking in speed.A novel method which uses noisy data from such a commoditysensor to build dynamic internal representations of faces isproposed. Distances to a surface normal to the face are measuredin real-time and used as input to a specific type of recurrentneural network, namely long short-term memory. This enablesthe prediction of facial structure in linear time and also increasesrobustness towards partial occlusions.