In this paper, we propose a disguised face recognition system based on Deep Normalization and Convolution Neural Network (DNCNN), this system include two trained DNCNN identification Network. The function of first trained identification network is to identify the type of disguised of the input face image. This network classifies human face disguised input images into three categories, No disguised, Upper half face disguised and Lower half face disguised. After the classification is completed, the system will remove the upper half disguised or the lower half disguised of the face image, and remaining the non-disguised half face images, then input it into the second recognition network. The function of the second recognition network is to recognize the identification of the input half face image. To reduce the over-fitting caused by imbalanced and insufficient training samples. Before performing the training and identification of the above two DNCNN recognition networks, we need to perform the pre-process on the original image samples first. The image pre-process is used the Viola-Jones face detection algorithm. The algorithm first finds out the block of the face position of original images, then the pre-process rotates and captures the face block image or half face images for the training and testing of recognition networks. After the preprocessing is completed, we can perform the training and testing of DNCNN recognition networks. The experimental results show that the system achieved a similar recognition rates as the reference.