Face recognition is a challenging task as it has to deal with several issues such as illumination orientation and variability among the different faces. Previous works have shown that 3D face is a robust biometric trait and is less sensitive to light and pose variations. Also due to availability of inexpensive sensors and new 3D data acquisition techniques it has become easy to capture 3D data. A 3D depth image of a face is found to be rich in information and biometric recognition performance can be enhanced by using 3D face data along with convolutional neural network. However the shortcoming of this approach is the conversion of 3D data to lower dimensions (depth image) which suffer from loss of geometric information and the network becomes computationally expensive. In this work we endeavor to apply deep learning method for 3D face recognition and propose a deep convolutional neural network based on PointNet architecture which consumes point cloud directly as input and siamese network for similarity learning. Further we propose a solution to the issue of a limited database by applying data augmentation at the point cloud level. Our proposed technique shows encouraging performance on Bosphorus and IIT Indore 3D face databases.

人脸识别是一项艰巨的任务，因为它必须处理多个问题，例如不同人脸的照明方向和可变性。先前的工作表明3D脸部是一种强大的生物特征，并且对光线和姿势变化不太敏感。同样由于廉价传感器的可用性和新的3D数据采集技术，捕获3D数据变得很容易。发现人脸的3D深度图像具有丰富的信息，并且可以通过将3D人脸数据与卷积神经网络一起使用来增强生物特征识别性能。然而，该方法的缺点是将3D数据转换为较低尺寸（深度图像），这会损失几何信息，并且网络在计算上变得昂贵。在这项工作中，我们努力将深度学习方法应用于3D人脸识别，并提出一种基于PointNet架构的深度卷积神经网络，该网络直接使用点云作为输入，并使用暹罗网络进行相似性学习。此外，我们通过在点云级别应用数据增强为有限的数据库问题提出了解决方案。我们提出的技术在Bosphorus和IIT Indore 3D人脸数据库上显示出令人鼓舞的性能。