Abstract: In different authentication scenarios, it is difficult to adapt the existing methods to face recognition under different authentication photos for the sake of the influence of age span, dress-up and lack of samples, which cannot conform to the practical application requirements. For the sake of solving the above problems, it puts forward a different identification method on the basis of the deep convolution neural network. This method makes the improvement of VGG network adapted to different document photo recognition, realizing end-to-end autonomous learning of face features, eliminating the influence of age span, dress-up and other factors. In addition, the method cuts down the trainable parameters to 1/6 of the original network structure, thus ensuring the identification accuracy while greatly reducing the training time of the model. According to the experimental results, after training on the self-built data set and CAS-PEAL-R1 public data set under the college graduation examination scene, the verification accuracy and recall rate of this method were 6.29 and 7 percentage points higher than the original method respectively, which can conform to the different document examination needs under various application scenarios.

Key words: face recognition, ID photo verification, convolutional neural network, face verification