基于卷积神经网络的大姿态人脸对齐方法

大姿态人脸对齐是人脸识别和三维人脸重构等很多重要视觉任务的先决条件。现有的对齐方法大多使用二维界标位置来进行对齐，且使用的界标数量有限，影响大姿态人脸对齐的准确性。提出一种采用三维形变模型(3DMM)来表示二维人脸图像，将具有任意姿态的人脸对齐问题建模为基于3DMM的拟合问题。采用基于卷积神经网络(CNN)的级联回归方法学习二维人脸图像及其表示之间的映射关系。提出2种新的姿态不变局部特征作为卷积神经网络学习的输入层，通过训练得到CNN用于大姿态人脸对齐。在2个经典的人脸图像数据集上的仿真实验结果表明，与目前最新的人脸对齐方法相比，该方法的效果较优。

Large pose face alignment method based on convolutional neural network

Large pose face alignment is a prerequisite for many important visual tasks such as face recognition and 3D face reconstruction. However, most of the existing alignment methods use two-dimensional boundary markers to align, and the number of boundary markers used is limited, which greatly affects the accuracy of large pose face alignment. Therefore, an improved large pose face alignment method is proposed. Firstly, 3D deformable model is utilized to represent 2D face image. And the problem of face alignment with arbitrary pose is modeled as a fitting problem based on Three Dimensional Deformation Model(3DMM). And then a cascade regression method based on Convolutional Neural Network(CNN) is adopted to learn the mapping relationship between two-dimensional face image and its representation. Finally, two new pose invariant local features are proposed as the input layer of CNN learning, and CNN is applied for large pose face alignment through training. Simulation results on two classic face image data sets show that the proposed method is better than the latest face alignment method.