| Summary: | With the rapid development of face recognition, the 3D face has gradually become the mainstream, 3D face point cloud quality judgment was an important process. A Feature Fusion Network (FFN) was proposed to judge 3D face point cloud quality acquired by binocular CCD camera. Firstly, the 3D point cloud was preprocessed to cut out the face area, and the image obtained from the point cloud and the corresponding 2D plane depth map projection was used as the 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.
|
|---|