计算机工程与应用2024，Vol.60Issue(5)：240-249,10.DOI:10.3778/j.issn.1002-8331.2210-0234
基于组残差块生成对抗网络的面部表情生成
Facial Expression Generation Based on Group Residual Block Generative Adversarial Network
摘要
Abstract
Facial expression generation is the generation of facial images with expressions through a certain expression calculation method,which is widely used in face editing,film and television production,and data augmentation.With the advent of generative adversarial network(GAN),facial expression generation has made significant progress,but problems such as overlapping,blurring,and lack of realism still occur in facial expression generation images.In order to address the above issues,group residuals with attention mechanism generative adversarial network(GRA-GAN)is proposed to generate high-quality facial expressions.Firstly,an adaptive mixed attention mechanism(MAT)is embedded in the generative network before downsampling and after upsampling to adaptively learn the key region features and enhance the learning of key regions of the image.Secondly,the idea of grouping is integrated into the residual network,and the group residuals block with attention mechanism(GRA)module is proposed to achieve better generation effect.Finally,the experimental verification is carried out on the public dataset RaFD.The experimental results show that the proposed GRA-GAN outper-forms the related methods in both qualitative and quantitative analysis.关键词
生成对抗网络/表情生成/注意力机制/组残差块Key words
generative adversarial network(GAN)/expression generation/attention mechanism/group residual block分类
信息技术与安全科学引用本文复制引用
林本旺,赵光哲,王雪平,李昊..基于组残差块生成对抗网络的面部表情生成[J].计算机工程与应用,2024,60(5):240-249,10.基金项目
国家自然科学基金(62176018). （62176018）
