Research And Application Of 3D Face Deformation Based On Deep Learning

3D face recognition technology is a widely used identity authentication technology,which has mature applications in security,online payment and other fields,and deep learning has a good performance in 3D face recognition.A good 3D face recognition method should have good robustness to changes in facial expressions,which requires a large amount of 3D face data under various expressions during the training process.However,due to the difficulty in obtaining 3D face data and the lack of accuracy of some 3D face data,the development of 3D face recognition technology is facing huge difficulties.Performing 3D face deformation from the existing 3D face data set to expand other 3D faces is a effective way to solve the problem of lack of 3D face data.This paper introduces the current main 3D face deformation methods,proposes a 3D face deformation method based on disentangled representation learning,and designs a 3D face acquisition system based on this method which provides a effective solution to solve the problem of lacking 3D face data.The main work of this paper is as follows:Firstly,in order to solve the problem of insufficient face samples for neutral expressions,this paper proposes a 3D face sample expansion method based on region reorganization and face interpolation which combines the advantages of two data expansion methods.This method can generate a large number of high quality 3D face samples with natural expression to expand the neutral expression 3D face data.It provides a large number of neutral 3D face samples for the study of 3D face deformation.Secondly,this paper studies and improves the 3D face disentangled representation algorithm based on VAE network.In order to get a better disentangled representation of 3D face,this paper improves the input mode and convolution mode of the network.In addition,this paper applies the algorithm to 3D face deformation.It can decouple a 3D face into identity and expression characteristics,and could obtain deformed 3D face from changing the expression characteristics and fusing with the identity characteristics.The method provides a way to deform the 3D face while keeping the identity features as unchanged as possible,and generate a realistic deformed 3D face,which provides a large and effective data basis for the research of 3D face recognition.This paper designs comparative experiments with three other deformation models on the public database.From the experimental results,the performance of several evaluation indicators of 3D face deformation is better,which confirms the feasibility of the method proposed in this paper.At last,this paper designs a 3D face acquisition system based on the 3D face deformation method which uses the 3D face deformation framework.The system uses a 3D camera to shoot human faces and build 3D models,and then deform and save 3D faces.It can store multiple 3D face models with different expressions from only once face collecting.The system could achieve the goal to greatly expand the 3D face data from the limited collection of 3D face data.This paper also developed a high-concurrency server framework based on the event response mechanism to provide sufficient and stable network service to the 3D face collection system.This framework is superior to the current mainstream high-concurrency server framework in many aspects,and has excellent performance.