Abstract: There exist large fuzziness of human face and large change of people’s gesture and other issues when images are collected,and the accuracy of face recognition is not high.In order to improve the accuracy of human face recognition,this paper proposed a new face recognition algorithm based on information enhancement of convolutional neural network.Wavelet denoising is applied to the collected fuzzy face images.Theadaptive template matching is given for output image affter noise reduction,and the face image is segmented by the image segmentation method.The geometric feature invariance of the Radon scale transform is used to implement information enhancement for the key feature points of the face.The convolutional neural network classifier is used to classify the enhanced facial feature points to realize feature point optimization and accurate face recognition.The experiments show that the accuracy of proposed method is better,and the proposed method can fulfill the application requirements of rapid recognition for large-scale sample faces.

Key words: Block, Convolutional neural networks, Face recognition, Image, Noise reduction, Radon scale transform