Research on occlusion block face recognition based on feature point location
Jianguo Shi, Yu Xiu, Ganyi Tang · Computer Animation and Virtual Worlds · 2022
Abstract Aiming at improving the poor face recognition accuracy under occlusion, a multipose block occlusion face recognition method based on feature point location is proposed. Face segmentation is carried out according to the location results of face feature points and occlusion areas. The local features of each face block are extracted by deep convolution neural network. The dynamic adaptive weighting method is used to give different weights to the face block information, and the occluded face recognition is completed according to the results of face segmentation, which effectively reduces the impact of pose change and occlusion on face recognition. The experiment is analyzed from two aspects: frontal occlusion and multipose face occlusion. The results show that fewer blocks in the frontal occlusion experiment are conducive to maintaining good local integrity and relatively good recognition performance; When the proportion of frontal occlusion reaches 50%, the recognition rate of our algorithm can still be up to 92.68%, which is significantly better than other algorithms.