A face recognition algorithm based on CNN with ELBP and DCGAN

Convolutional neural network (CNN) have been widely used in face recognition. The illumination and the size of the data set affect the accuracy of face recognition based on CNN. In order to improve the accuracy of face recognition based on CNN, an improved face recognition algorithm based on CNN with extend local binary pattern (ELBP) and deep convolutional generative adversarial network (DCGAN) is proposed. ELBP uses a circular operator which can be of any size, and the coverage area can be adjusted arbitrarily. It has gray and rotation invariance, and has good robustness to illumination. In real scenes, it is more difficult to obtain face photos of a person with different lighting and different scenes. In order to solve face recognition in the case of small data sets, this paper uses DCGAN to generate new face pictures based on the original pictures. By expanding the data set, the accuracy of face recognition is improved. Experiments have proved that the proposed method in this paper has higher accuracy in face recognition than traditional methods.