中文摘要：

传统的AAM（Active Appearance Models）人脸特征定位改进方法通常关注于拟合效率上,没有具体考虑拟合初始位置和模型实例的特征,因此定位准确率和速度并不理想。该文提出了一种基于人脸特征检测和简单三维姿态估计的拟合初始位置改进和模型实例选择方法。首先采用Adaboost算法对图像中人脸特征进行预检测,然后充分利用YCbCr色彩空间人脸肤色特性对无法检测或检测不完全的图像进行特征提取,最后根据特征区域计算鼻尖坐标和人脸偏转角,合理调整拟合中心位置和模型实例,并在拟合过程中引入ATLAS（Automatically Tuned Linear Algebra Software）线性代数软件包,实现矩阵优化。基于IMM人脸库的仿真实验表明,该方法与传统反向组合AAM相比,拟合准确率提高约43%,时间消耗降低约62%。

英文摘要：

Traditional AAM （Active Appearance Models） improved methods on human facial features localization always concentrate on fitting efficiency without any concrete analysis of characteristic of the initial position and model instance, thus the location accuracy and speed are both not ideal. An initial position correction and model instance selection method based on facial features detection and simple 3D pose estimation is proposed. Adaboost algorithm is applied to pre-detection of facial features in the images firstly, then to extract features from the images that could not be detected or have been incompletely detected using facial skin properties in YCbCr color space. Finally, calculate the coordinate of the nose tip and deflection angle of the face according to features region, properly adjust the fitting center position and model instance and introduce linear algebra software ATLAS（Automatically Tuned Linear Algebra Software） into fitting process for matrixes optimization. Simulation experiments on IMM face database show that proposed method has increased the fitting accuracy rate by about 43% and the time consumption is decreased by about 62% comparing with traditional Inverse Compositional AAM Algorithm.