两因子模型在多姿态人脸识别中的应用

Application of Two-factors Model in Multi-Pose Face Recognition

通过对两因子模型的进一步深化、提炼和改进,提出了一种基于两因子模型的多姿态人脸识别方法,该方法能有效地缓解人脸特征对姿态变化较为敏感的问题。实验结果表明,经过姿态因子分离后的人脸全局或局部特征在保持较高显著性的同时,均对姿态变化具有理想的鲁棒性,在FERET人脸数据库上取得了最高92.5%的识别率。

We present a multi-pose face recognition method based on two-factor analysis model.The method refines the traditional two-factor analysis model,and partly solves the problem that facial feature is sensitive to the pose variation.Large number of 3D face data is trained in the two-factor analysis model to get robust and differential pose factors.In the experiment of FERET facial database,the best recognition accuracy is 92.5%.The results show that both the global and local facial features maintain a good pose variation robustness and high significance of feature descriptor after the pose factor separation.