CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor郭大蕾
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword人脸关键点标记 人脸识别 头部姿态估计 人机交互
Abstract    面部特征识别和头部姿态估计可根据图像提供用户情绪状态、视线方向、操作意图等大量隐含信息,在心理分析和人机交互等领域应用前景广阔。人脸关键点标记作为面部图像分析中的基础研究,是面部特征识别和头部姿态估计的重要前提。本论文从最优化求解角度,围绕人脸关键点标记问题及其在面部特征识别和头部姿态估计中的应用进行研究,主要内容如下:
    首先,研究了人脸关键点标记问题。针对图像特征描述信息冗余问题,提出了PCA-SDM(Principal Component Analysis-Supervised Descent Method)人脸关键点标记方法,通过主成分分析将高维特征投影至低维子空间,缩短了模型训练时间,提升了关键点标记准确度。为了提高关键点标记鲁棒性,建立了联合表观和位置特征的asSDM(appearance-shape-Supervised Descent Method)关键点标记模型,借助岭回归法求解位置偏量,降低了关键点标记误差。
Other Abstract    In the field of computer vision, face alignment is among the most popular and well-studied problem. Accurate and efficient alignment algorithm provides a benchmark for face recognition, head pose estimation, facial expression analysis and 3D face modeling. This thesis employs the supervised descent method as the major tool, studies the face alignment issue and the application of facial feature detection and head pose estimation. The major contributions of this thesis are as follow:
    Firstly, face alignment algorithm is studied. Face alignment can be solved as a nonlinear optimization problem. This thesis proposes a modified method PCA-SDM(Principal Component Analysis-Supervised Descent Method) to extract the effective information and weak the noise by selecting the appropriate dimension. Most of the existing methods only rely on the current facial texture and it is unreliable when facial landmarks are partially occluded in unconstrained scenarios . To settle the issue asSDM(appearance-shape-Supervised Descent Method) is proposed , utilizing both appearance and shape information in learning regression functions. The performance of the proposed method is evaluated on different data sets and the results on benchmark databases demonstrate that the proposed method outperforms previous work for facial landmark detection.
    Secondly, the applications of facial feature recognition based on face alignment are studied. According to the facial key points, the eye state can be discriminated. Using texture information at the key points as facial feature instead of the single pixel value in an image, can achieve higher accuracy in face recognition algorithm, especially dealing with the images where there is a large variety on pose and expression.
    Thirdly, the applications of head pose estimation and motion detection based on face alignment and track are studied. By constructing the mapping from 3D model to 2D plane, head pose can be estimated using a continuous angular measurement across multiple degrees of freedom.
    Finally, utilizing head pose information to control the computer can provide users with a more natural, convenient and comfortable operating experience.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
成艺. 基于人脸关键点标记的特征识别及姿态估计[D]. 北京. 中国科学院研究生院,2017.
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