|Place of Conferral||中国科学院自动化研究所|
|Keyword||人脸属性分析 人脸表情编辑 人脸关键点 人脸解析 生成对抗网络 人脸识别|
Facial images as one of the most common types of images are available in our daily life, so there is no doubt that it is convenient for researchers to do some algorithms design and applications developments based on such images. Apart from that, face contains a variety of attributes, including identity, age, facial expression and makeup, etc. The problems related to face have drawn much attention from both academic community and industries. In particular, with the rapid development of deep learning technology driven by big data, many tasks in various fields have been advanced a lot. Some comparatively mature techniques, such as face recognition and face image synthesis, have changed our life tremendously in a positive way, making the public have a sense of satisfaction of artificial intelligence. However, owing to unavoidable texture and details variations caused by facial movements, environment and time, it is challengeable to synthesize and edit facial images with specific attributes to obtain high-quality and approximately realistic results.
To make it simple, facial attribute analysis can be divided into two parts: attribute recognition, and attribute editing which can promote the progress of attribute recognition. This project will focus on attribute editing, especially facial expression editing. More concretely, based on generative adversarial network, this work will explore the performances of distinct network structures, with the purpose of designing more generalized models to solve expression editing. The contributions of this thesis are summarized as follows:
• This thesis proposes a generative adversarial network based method editing facial expressions, guided by facial landmarks. Specifically, two sub generative adversarial networks are fused into one framework to simultaneously address two opposite tasks: expression synthesis and expression removal, which brings three benefits: 1) stabilizing the process of training and improving the quality of edited facial images; 2) combining the traditional method to transfer expressions between two people; 3) keeping the performance of face recognition using the expression removal branch when facing expression variations.
• An architecture based on generative adversarial theory is introduced to cope with face parsing prediction and facial expression editing. This work utilizes controllable variations to control the procedure of face parsing prediction. After that, the predicted results can assist facial expression editing. It is true that users can conveniently control features of expected results, both the kinds of expressions and degrees of expressions,
|卢治合. 基于对抗学习的人脸表情编辑方法研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2019.|
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