Weighted Multi-task Sparse Representation Classifier for 3D Face Recognition

Rapid development of 3D face recognition can help people overcome some bottlenecks in 2D recognition. But still susceptible to changes in facial expressions. At the same time, due to the large number of 3D point clouds, the calculation speed is also greatly affected. This paper mainly proposes a method to classify 3D human faces according to the characteristics of their semi-rigid and non-rigid regions to enhance the robustness of recognition of 3Dfacial expression changes. At the same time, improve the expression of the 3D point cloud face, reduce the number of points involved in the calculation, and increase the speed of the algorithm. Experimental results show that the algorithm not only has a higher recognition rate but also has stronger robustness to changes in facial expression.