Convolution neural network face recognition based on fractional differential

Abstract:

In recent years, face recognition technology as a powerful means to capture biological facial feature information and match face data in existing databases has been applied in more and more scenes with its advantages of non-contact and remote implementation. Due to the influence of illumination, posture, background environment and other factors under the natural unconstrained condition, the recognition rate of the face images captured by the devices is still insufficient in the existing face recognition model. This paper proposes a face recognition method based on fractional differential improved residual network (ResNet), by adding attention mechanism to the original network model structure. At the same time, fractional differential is used to process node functions, and convolution blocks are added to extract more face details. Finally, Arcface loss function is used to optimize the model, and iterative training is carried out in the network to complete face recognition. The experimental results show that the proposed model has better recognition performance and stronger robustness on the existing data sets such as LFW, AgeDB-30, CFP-FP.