Abstract：Focused on the low positioning accuracy of Explicit Shape Regression (ESR) for some facical occlusion and excessive facial expression samples, an adaptive window regression method was proposed. Firstly, the priori information was used to generate an accurate face area box for each image, feature mapping of faces was performed by using the center point of the face area box, and similar transformation was performed to obtain multiple initial shapes. Secondly, an adaptive window adjustment strategy was given, in which the feature window size was adaptively adjusted based on the mean square error of the previous regression. Finally, based on the feature selection strategy of Mutual Information (MI), a new correlation calculation method was proposed, and the most relevant features were selected in the candidate pixel set. On the three public datasets LFPW, HELEN and COFW, the positioning accuracy of the proposed method is increased by 7.52%, 5.72% and 5.89% respectively compared to ESR algorithm. The experimental results show that the adaptive window regression method can effectively improve the positioning accuracy of face feature points.
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2019, Vol. 39 
