改进的显式形状回归人脸特征点定位算法

摘要

To solve the problem that Explicit Shape Regression (ESR) has low precision in face alignment,an improved explicit shape regression for face alignment algorithm was proposed.Firstly,in order to get a more accurate initial shape,threepoint face shape was used as an initial shape mapping standard to replace face rectangle.Then,pixel block feature was used against illumination variations instead of pixel feature,which improved the algorithm robustness.Finally,instead of average method,the accuracy of algorithm was further improved by multiple hypothesis fusion strategy which merged multiple estimations.Compared with explicit shape regression algorithm,the simulation experimental results show that the accuracy is improved by 7.96％,5.36％ and 1.94％ respectively by using the proposed algorithm on LFPW,HELEN and 300-W face datasets.%针对显式形状回归(ESR)人脸特征点定位精度低的问题,提出了改进的显式形状回归人脸特征点定位算法.首先定位出三点人脸形状代替人脸检测框作为初始形状的映射标准来得到更精确的初始人脸形状,然后采用像素块特征代替像素特征对抗光照变化来提高算法的鲁棒性,最后采用多假设融合策略代替平均法对多个定位结果进行最佳融合来进一步提高算法的定位精度.仿真实验结果表明,在LFPW、HELEN和300-W人脸库上,与显式形状回归算法相比,定位精度分别提高了7.96％、5.36％和1.94％.