Research On Facial Expression Conversion Technology And Application Based On Cycle Generative Adversarial Networks

Image conversion technology has important application value in life.If you can use facial expression conversion function in mobile applications,it will provide you with a more convenient experience.With the development of deep learning,Generative Adversarial Networks(GAN)is widely used in the field of image processing.However,the traditional GAN still has problems such as unstable training process and unpaired training images in image conversion research,which limits the development of image conversion technology to a certain extent.In order to solve these problems,this thesis designs an image conversion mechanism based on Cycle Generative Adversarial Networks(Cycle-GAN),and implements it in mobile devices.The main work is summarized as follows:1.For the image conversion task of facial expression from non-smile to smile,this thesis uses Cycle-GAN algorithm.Which ensures that the generated image shares the feature of irrelevant area with the original image,making the generated image more realistic;Accelerate convergence by combining the cycle consistency loss with the adversarial loss in image conversion,improving training efficiency.The GAN global training affects the part of the image that should not be changed.This thesis controls the influence of the model on the unrelated regions by generating a mask.2.Move to mobile for complex deep learning frameworks.This thesis proposes the method of encapsulating the model into an API,which avoids the problem that the mobile terminal does not adapt the internal function support of the native model.Based on the B/S mode,the PyTorch is combined with the GPU acceleration mechanism on the PC server to build a Cycle-GAN model.And use the Flask framework with the Bootstrap framework to achieve front-to-back data transfer.Finally,the entire project is packaged into an Android application using the Apache Cordova method in WebView.The results show that Cycle-GAN has a good performance in facial expression conversion applications.At the same time,the mechanism can be easily transplanted to the mobile terminal,and the application runs fast and the memory usage is small.It can be seen that this is a development solution with excellent feasibility and wide application prospects.