New face recognition technologies based on 3DMM

Abstract

Face recognition system includes face detection, face positioning, and face identification. ith the advent of the information age, identity authentication has become more and more important, and face recognition has become the mainstream method of identity verification due to its non-invasiveness, easy availability, and high reliability. With the current rapid development of artificial intelligence, it is of practical significance to introduce deep learning methods into face recognition. My work uses the caffe framework to design an 18-layer network model. At the same time, 448,808 pictures of 1,583 objects in the YouTube Face data set were used as training set, and 111,403 pictures of 1583 objects were used as verification sets. After preprocessing. These images, we entered them into the network for training our model. The facerecognition accuracy of the final model reached 99.7%. Next, my work did a forward-looking work for 3D face verification: single-view 3D face reconstruction. At present, the lack of open source 3D face database can be said to be the biggest obstacle to 3D face verification if you want to use deep learning algorithms. In order to solve this problem, my work will focus on the reconstruction of 3D human faces. Traditional 3D face reconstruction methods are either unstable or over-regularized. So I tried to apply the deep learning method to the 3D face reconstruction. First of all, in order to solve the problem of lacking 3D model data, we improved the current Multi-view 3D face reconstruction methods, using the 3D Morphable Models (3DMM) to generate huge numbers of labeled examples. Next, design the network and train it, the network finally implemented the function of constructing a 3D model of the object from a 2D picture of itself.