3D face recognition with reconstructed faces from a collection of 2D images

Abstract

Nowadays, there is an increasing need for systems that can accurately and quickly identify a person. Traditional identification methods utilize something a person knows or something a person has. This kind of methods has several drawbacks, being the main one the fact that it is impossible to detect an imposter who uses genuine credentials to pass as a genuine person. One way to solve these kinds of problems is to utilize biometric identification. The face is one of the biometric features that best suits the covert identification. However, in general, biometric systems based on 2D face recognition perform very poorly in unconstrained environments, common in covert identification scenarios, since the input images present variations in pose, illumination, and facial expressions. One way to mitigate this problem is to use 3D face data, but the current 3D scanners are expensive and require a lot of cooperation from people being identified. Therefore, in this work, we propose an approach based on local descriptors for 3D Face Recognition based on 3D face models reconstructed from collections of 2D images. Initial results show 95% in a subset of the LFW Face dataset.