随着计算机视觉领域的快速发展，人脸信息处理在人脸图像合成、视觉疲劳检测等应用领域发挥着越来越重要的作用。光照、姿态、表情等多种因素使得人脸图像存在复杂性，而人脸显著特征点的精确定位能提高复杂情况下的多姿态人脸合成与视觉疲劳检测的效果。因此，本文研究基于人脸图像显著特征点定位的多姿态人脸图像的合成和视觉疲劳检测。取得的主要创新性研究进展概括如下：针对基于混合树模型特征点定位不精确和基于监督下降法中人脸漏检率较高的问题，提出了将混合树模型与监督下降法相结合的多姿态人脸图像检测与特征点定位算法。该方法采用混合树模型对多姿态人脸图像进行初定位，通过监督下降法对人脸五官轮廓特征点进行精准定位，结合混合树模型得到的人脸外轮廓特征点得到整张人脸图像的显著特征点。本文提出的方法能精确定位多姿态人脸的外轮廓和五官轮廓特征点。针对多姿态人脸合成中人脸姿态空间的非线性难以表示的问题，结合张量分析和姿态流形建模，提出一种改进的多姿态人脸图像合成算法。该方法通过对多姿态训练集人脸的形状信息进行张量分解，在姿态子空间通过样条拟合得到人脸形状的姿态流形，在身份子空间通过稀疏表示合成出测试图像的身份信息，用于合成新身份下的形状姿态流形。针对眼角和鼻翼因视角的转动而造成的图像拉伸的问题，通过几何关系构造4个新的特征点的方法进行优化，最后通过仿射变换将正面测试人脸的纹理映射到形状姿态流形上，合成多姿态人脸图像。实验结果表明，所提方法得到的多姿态人脸合成图像较好的保持了测试人脸的形状和身份信息，并且合成的人脸纹理逼真、自然。针对基于人脸的视觉疲劳检测算法需要快速准确的定位人脸的显著特征点，本文通过Adaboost人脸检测器对人脸进行初定位，利用监督下降法精确定位和跟踪视频中的人脸特征点，并根据双眼面积眼距比、嘴部高宽比来制定视觉疲劳判定准则，从而提出基于监督下降法的视觉疲劳检测算法。通过在驾驶视频和仿真视频上测试的结果表明本文的方法能够准确的判断视觉疲劳。

With the rapid development of computer vision technology, human facial information processing plays an important role in fields of human face synthesis and visual fatigue detection. The factors like illumination, pose and expression variations make human face image more complicated. Accurate locating of human facial landmarks can improve the effect of multi-pose human face synthesis and visual fatigue detection. Therefore, we focus on multi-pose human face synthesis and visual fatigue detection based on human facial landmark locating in this thesis. The main achivements are summerized as follows. To address the problems of inaccurate locating of facial landmarks in the mixtures of tree model and missing detection of faces in Supervised Descent Method (SDM), we propose a multi-pose human face detection and facial landmark locating algorithm by combining the mixtures of tree model with SDM. We initialize the localization of multi-pose faces by the mixtures of tree model, then locate and refine the landmarks of inner facial contour by SDM, which are combined with the outer facial contour to achieve all facial landmarks of multi-pose faces accurately. To solve nonlinear representation of the pose subspace in multi-pose human face synthesis, we propose a modified multi-pose face synthesis algorithm. We apply tensor decomposition on the shapes of multi-pose faces in the training set to separate the pose and identity subspaces. We get the pose manifold by spline fitting in the pose subspace. The identity coefficient of the test image is synthesized through sparse representation in identity subspace. Based on the pose manifold and the synthesized identity coefficient, we obtain the shape manifold of the new identity. Aimed at image stretching in the corner of eyes and nose caused by pose variation, we add four additional landmarks using the geometrical relationships of facial landmarks to modify the results. Finally, we warp the texture of the test frontal face image to the shape manifold through affine transformation to synthesize the multi-pose faces of the test image. The synthesized multi-pose face images in our method maintain the shape and identity of the test image very well. The synthesized facial texture looks natural and real.Since fast and accurate facial landmark locating is urgent in visual fatigue detection based on human face, we initialize the localization of human face by Adaboost based human face detector, then localize and track human facial landmarks in the video by SDM. We set a series of rules for visual fatigue detection according to the ratio of eye area to eye distance and the aspect ratio of mouth based on the facial landmarks. The experimental results on driving videos and simulation videos show the superiority of our method in visual fatigue detection.