Facial expression generation based on variational AutoEncoder network and cloud computing

Abstract

With the rapid development of portable terminal devices, such as smartphones, the development of expression generation technology makes the emotional communication between people in social networks more rich and diverse. However, there are still great challenges in deploying deep networks on mobile phones with limited resources. Moreover, the muscle movement of the face produces different facial expressions. To this end, this paper proposes an efficient emotion image generation framework based on Transformer and variational auto-encoders (VAE) on the cloud platform. Specifically, we collect human face which is further transferred to the service. To exploit connections between muscles and expressions, we introduce the multi-head attention mechanism to construct Transformer. To generate different emotional images, we design a novel emotion generation model named Transformer-embedded VAE (TEVAE). All the experimental results show that the proposed TEVAE obtains higher performance in emotion image generation tasks compared to VAE.