Research on face recognition method based on deep learning in natural environment

In the present study, there are a number of recognition methods with high recognition accuracy, which are based on deep learning. However, these methods usually have a good effect in a restricted environment, but in the natural environment, the accuracy of face recognition has decreased significantly, especially in the case of occlusion, face recognition will appear inaccurate or unrecognized situation. Based on this, this paper presents a face recognition method based on the deep learning in the natural environment, hoping to achieve robust performance in the natural environment, especially in the case of occlusion. The main contribution of this paper is improving the method of multi-patches by using 4 areas' patches in the face. And in order to have a higher performance, we use a Joint Bayesian (JB) measure in face-verification. Finally, we trained the model by the set of CASIA-WebFace and test it in the Labeled Faces in the Wild (LFW).