Multi-channel Deep 3D Face Recognition

• URL: http://arxiv.org/abs/2009.14743v1
• Date: Wed, 30 Sep 2020 15:29:05 GMT
• Title: Multi-channel Deep 3D Face Recognition
• Authors: Zhiqian You, Tingting Yang, Miao Jin
• Abstract summary: The accuracy of 2D face recognition is still challenged by the change of pose, illumination, make-up, and expression. We propose a multi-Channel deep 3D face network for face recognition based on 3D face data. The face recognition accuracy of the multi-Channel deep 3D face network has achieved 98.6.
• Score: 4.726009758066045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition has been of great importance in many applications as a biometric for its throughput, convenience, and non-invasiveness. Recent advancements in deep Convolutional Neural Network (CNN) architectures have boosted significantly the performance of face recognition based on two-dimensional (2D) facial texture images and outperformed the previous state of the art using conventional methods. However, the accuracy of 2D face recognition is still challenged by the change of pose, illumination, make-up, and expression. On the other hand, the geometric information contained in three-dimensional (3D) face data has the potential to overcome the fundamental limitations of 2D face data. We propose a multi-Channel deep 3D face network for face recognition based on 3D face data. We compute the geometric information of a 3D face based on its piecewise-linear triangular mesh structure and then conformally flatten geometric information along with the color from 3D to 2D plane to leverage the state-of-the-art deep CNN architectures. We modify the input layer of the network to take images with nine channels instead of three only such that more geometric information can be explicitly fed to it. We pre-train the network using images from the VGG-Face \cite{Parkhi2015} and then fine-tune it with the generated multi-channel face images. The face recognition accuracy of the multi-Channel deep 3D face network has achieved 98.6. The experimental results also clearly show that the network performs much better when a 9-channel image is flattened to plane based on the conformal map compared with the orthographic projection.

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