Off-line Chinese Signature Verification Using Convolutional Neural Network

Background: The unique individual biological characteristics is used for identification in biometrics, which is safe and difficult to forge. Therefore, it can help to enhance the safety of access control system. Since the developing of modern information science and technology, computerbased signature verification system enables signature verification more efficiently and automatically in comparison with traditional human identification method. Methods: In order to improve the accuracy of Chinese signature verification, an off-line Chinese signature verification method based on deep convolutional neural network is proposed. First, the machine learning library Tensorflow is build, and the volunteers are invited to establish the offline Chinese signature dataset. Second, the dataset is pre-processed, including denoising, binarization and size normalization. Finally, three different CNN architectures (AlexNet, GoogleNet, VGGNet) are adopted to implement the signature verification. Results: Experimental results show that the performance of AlexNet is better than that of the other two convolutional neural network architectures, the accuracy of classification has been up to 99.77%, and verification rate is 87.5%. Conclusion: Compared with the traditional offline Chinese signature recognition method, the method based on the convolutional neural network Alex Net-f is better than other methods to some extent, and avoids the complicated feature engineering.