关键词: 人脸特征点定位, 级联姿态回归, 随机森林, 全局线性回归, 多视角生成模型

Abstract: Aiming at the problem of low precision when locating facial landmarks due to large pose variations in face images, a multi-view face alignment algorithm is proposed. Cascaded Pose Regression（CPR） model is used to establish many different models under multi-view face images, which combines local learning principle with random forest and global linear regression. Multi-view models are established to improve face alignment accuracy, replacing a single model. Firstly, CPR model is used to establish different models for multi-view face images. Then, multi-view generative model is used to estimate the face pose of an input face image. Finally, according to face pose, a corresponding model is selected for an input image, which achieves high precision for face alignment. The experimental results show that the proposed face alignment algorithm has higher location precision than several existing face alignment algorithms.

Key words: face alignment, cascaded pose regression, random forest, global linear regression, multi-view generative model