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Thesis Advisor张晓鹏
Degree Grantor中国科学院大学
Place of Conferral北京
Keyword多任务学习 人脸检测 关键点定位 语义分割 图像合成
Abstract  个人肖像图像是社交网络图片分享的重要组成部分,然而受拍摄环境、摄影设备和用户自身摄影技巧等因素的影响,实景拍摄的人像图片质量普遍不高。图像合成技术被广泛应用于生成符合用户个人特点且具美学吸引力的高质量图像。作为妆容合成美化的基础,人脸图像分析技术通过对人像进行建模完成信息抽取及解析。现有基于人脸关键点的妆容合成方法,以人像五官关键点为基础进行妆容虚拟合成,在处理面部皮肤及头发等区域时普遍存在信息残缺等问题,妆容合成质量及稳定性有待提高。
Other Abstract  Portrait images play an important role in information sharing in social networks. However, due to restricted factors such as shooting environment, photography equipment or users' photography skills, the quality of portrait shots is generally not satisfying. Image synthesis techniques are widely used to generate high quality images that are more aesthetically appealing to user's personal preference. Face image analysis techniques, as the basis of makeup, perform facial information extraction and analysis by statistically modeling the human portraits. Existing face alignment based visual methods of makeup synthesis perform poorly when dealing with some facial regions such as facial skin and hair. The quality and stability of these methods need to be improved.
  In this thesis, we propose a novel makeup synthesis method based on semantic face segmentation. Taking the attributes of facial structure and related tasks of face analysis methods such as face detection, face alignment and face segmentation into account, we propose to use deep multi-task learning framework. To further validate our model, we show applications of makeup synthesis. The main contributions are summarized as follows:
  To the best of our knowledge, we are the first to present a deep multi-task framework that solves face alignment and face segmentation tasks jointly. We incorporate refinement module into our multi-task learning framework in order to parse the contours of facial parts more exactly. With a carefully designed refinement residual module, the cross-layer features are fused in a collaborative manner.
  We present a deep multi-task learning framework for face detection and face segmentation. A multi-scale residual module with good gradient flow is proposed to make the decision function more discriminative without excessive increase in the parameters of the network. We study the effect of downsampling module on the performance of the multi-task network.
  We design applications based on the face analysis results. Virtual makeup is achieved based on the result of face alignment and face segmentation. A skin smoothing and hair dyeing application is performed based on face detection and face segmentation result. Then the face swap can be completed based on face alignment result.
Subject Area计算机视觉
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
赵昱程. 人脸图像分析与妆容图像合成[D]. 北京. 中国科学院大学,2018.
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人脸图像分析与妆容图像合成-签名.pdf(10395KB)学位论文 暂不开放CC BY-NC-SAApplication Full Text
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