Convolutional neural network has been successfully applied to face recognition, but the extracted features ignore the local features of the face. In order to extract more comprehensive facial features, a convolutional neural network based on feature fusion for face recognition is proposed. This method takes the low frequency coefficients of the face images obtained by performing discrete cosine transform as global feature of the face. Besides, extracting local binary pattern features of original face images as local features of the face. Likewise, the image obtained by weighted fusion of global and local features is fed into the convolutional neural network for training. Experimental results in ORL and CAS-PEAL database show that the proposed method can improve the accuracy of face recognition.