The accuracy of existing face recognition models cannot improve due to the influence of masks and other occlusion factors. The current mainstream research methods integrate and apply the occluded and unoccluded scenes to multiple scenes after separate training. Aiming at the limitation of occluded face recognition model, this paper proposed an improved face feature rectification network(FFR-Net) model. This model could be used for face recognition with or without occlusion, and be applied to mask and glasses occlusion recognition scenes. FFR-Net proposed a face feature rectification module. In order to make full use of the feature information of the unocclusion area, the spatial branch of the module introduced involution operator to expand the image information interaction area and enhance the face feature information in the spatial range. The channel branch introduced coordinate attention to capture cross channel information to enhance the feature representation, which was conducive for the model to locate and identify the target area more accurately. Using Meta-ACON as a new dynamic activation function, it improved model generalization and calculation accuracy by dynamically adjusting the degree of linearity or nonlinearity. Finally, this paper trained the improved FFR-Net on the CASIA-Webface processed face dataset with or without mask occlusion. The accuracy of the test results on the LFW processed face dataset with or without mask occlusion and Meglass dataset are 82.50% and 89.75% respectively, which is superior to the existing algorithm, and verifies the effectiveness of the proposed method.

[2022-12-07] Accepted Paper /doc/2022.08.0447/v1

[2023-05-05] Printed Article /doc/2022.08.0447/v2