论文标题
重建大型3D面部数据集，以进行深3D面识别
Reconstructing A Large Scale 3D Face Dataset for Deep 3D Face Identification
论文作者
论文摘要
深度学习方法为计算机视觉带来了许多突破，尤其是在2D面部识别中。但是，基于深度学习的3D面部识别的瓶颈是，无论是用于行业还是学术界，都很难收集数百万个面孔。鉴于这种情况，有许多方法可以通过3D面部数据增强从现有的3D面上产生更多的3D面，这些面孔用于训练深3D面部识别模型。但是，据我们所知，没有方法可以从2D面图像中生成3D面，以训练深3D面部识别模型。这封信的重点是重建的3D面部表面在3D面识别中的作用，并提出了一个2D辅助深3D面识别的框架。特别是，我们建议使用基于深度学习的3D面部重建方法（即Expnet）重建大规模2D面部数据库（即VGGFACE2）的数百万个3D面部扫描。然后，我们采用了两阶段训练方法：在第一阶段，我们使用数百万的面部图像预先培训深卷积神经网络（DCNN），在第二阶段，我们使用重建的3D面部扫描的正常组件图像（NCI）来训练DCNN。广泛的实验结果表明，与由2D Face Images训练的模型相比，所提出的方法可以大大提高FRGC v2.0，Bosphorus和BU-3DFE 3D面部数据库的3D面部识别的排名1得分。最后，我们提出的方法在FRGC v2.0（97.6％），Bosphorus（98.4％）和BU-3DFE（98.8％）数据库中获得了最先进的排名1分。实验结果表明，重建的3D面部表面很有用，我们的2D辅助3D面部识别框架具有有意义的3D面孔。
Deep learning methods have brought many breakthroughs to computer vision, especially in 2D face recognition. However, the bottleneck of deep learning based 3D face recognition is that it is difficult to collect millions of 3D faces, whether for industry or academia. In view of this situation, there are many methods to generate more 3D faces from existing 3D faces through 3D face data augmentation, which are used to train deep 3D face recognition models. However, to the best of our knowledge, there is no method to generate 3D faces from 2D face images for training deep 3D face recognition models. This letter focuses on the role of reconstructed 3D facial surfaces in 3D face identification and proposes a framework of 2D-aided deep 3D face identification. In particular, we propose to reconstruct millions of 3D face scans from a large scale 2D face database (i.e.VGGFace2), using a deep learning based 3D face reconstruction method (i.e.ExpNet). Then, we adopt a two-phase training approach: In the first phase, we use millions of face images to pre-train the deep convolutional neural network (DCNN), and in the second phase, we use normal component images (NCI) of reconstructed 3D face scans to train the DCNN. Extensive experimental results illustrate that the proposed approach can greatly improve the rank-1 score of 3D face identification on the FRGC v2.0, the Bosphorus, and the BU-3DFE 3D face databases, compared to the model trained by 2D face images. Finally, our proposed approach achieves state-of-the-art rank-1 scores on the FRGC v2.0 (97.6%), Bosphorus (98.4%), and BU-3DFE (98.8%) databases. The experimental results show that the reconstructed 3D facial surfaces are useful and our 2D-aided deep 3D face identification framework is meaningful, facing the scarcity of 3D faces.
