Quality Judgment of 3D Face Point Cloud Based on Feature Fusion

Abstract

3D face point cloud quality judgment is an important task in 3D face recognition and 3D construction, as an auxiliary task of face recognition to predict the quality of a given image. However, projection-based quality judgment methods always ignore the value of multi-view 2D image features and 3D point cloud features. We propose a Feature Fusion Network (FFN) to address the above problems. We first preprocess the 3D point cloud obtained by the CCD camera, cut out the face area, and then use the point cloud data for multi-angle rotation and the corresponding 2D plane depth map projection as input. Secondly, Dynamic Graph Convolutional Neural Network (DGCNN) was trained for point cloud learning and ShuffleNet was trained for image learning. Then, the middle layer features of the two network modules were extracted and concat to fine-tune the whole network. Finally, three fully connected layers were used to realize the five-class classification of the 3D face point cloud (excellent, ordinary, stripe, burr, deformation). The proposed FFN achieved the classification accuracy of 83.7%, which was 5.8% higher than that of ShuffleNet and 2.2% higher than that of DGCNN. The experimental results show that concat depth map features and point cloud features can achieve the complementary effect between different features.