To solve the problem of unreasonable structure and low efficiency of the example block-based image inpainting method, a method for face image inpainting based on circular fields of feature parts was proposed. Firstly, according to the distribution of feature points obtained by feature points localization, the face image was segmented into four circular fields to determine feature search domains. Then, in priority model, the attenuation trend of confidence term was changed in form of exponential function, and with the combination of structural gradient term, the priority was constrained by using local gradient information to improve structural connectivity of inpainting result. In the stage of matching patch search, according to relative position between target patch and each circular domain of feature part, the search domain of matching patch was determined to improve search efficiency. Finally, under the standard of structural similarity, face image inpainting with structural connectivity was completed by choosing the best matching patch. Compared with four state-of-the-art inpainting methods, the proposed method has the Peak Signal-to-Noise Ratio (PSNR) of inpainted image increased by 1.219 to 2.663 dB on average, and the time consumption reduced by 34.7% to 69.6% on average. The experimental results show that the proposed method is effective in maintaining structural connectivity and visual rationality of face image, and has excellent performance in accuracy and time consumption of inpainting.

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.