Off-line Chinese Signature Verification Using Convolutional Neural Network

Author(s): Shao Hong, Xu Rongze, Guo Xiaopeng, Cui Wencheng*.

Journal Name: Recent Patents on Engineering

Volume 13 , Issue 4 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


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.

Keywords: AlexNet, convolutional neural network, GoogleNet, off-line signature verification, VGGNet, chinese signature.

P. Ding, G.Q. Li, and H.E. Sheng, Off-line Chinese signature identification method.. CN1219271, 2005.
Kejin Biometrics Shenzhen Co Ltd, Recognition method for off-line signatures. CN104134066A. 2014
A. Karouni, B. Daya, and S. Bahlak, "Offline signature recognition using neural networks approach", Procedia Comput. Sci., vol. 3, pp. 155-161, 2011.
Y. Danyue, and L. Yue, "Directional signature and grid feature fusion", J. Image Graph, vol. 17, pp. 717-721, 2012.
L. Hongtao, and Z. Qinchuan, "A Survey of the application of deep convolution neural network in computer vision", Data Collect. Process., vol. 31, pp. 1-17, 2016.
H. Khalajzadeh, M. Mansouri, and M. Teshnehlab, "Persian signature verification using convolutional neural networks. In ", International conference on pattern recognition, 2000. Proceedings on IEEE Xplore,, 2000pp. 851-854
L.G. Hafemann, R. Sabourin, and L.S. Oliveira, "Writer-independent feature learning for offline signature verification using deep convolutional neural networks In ", Neural Networks (IJCNN), 2016 International joint conference. IEEE 2016, pp. 2576-2583
Z. Wang, and M.T. Gao, "Design and implementation of image recognition algorithm based on convolution neural network", Modern Comp., vol. 20, pp. 61-66, 2015.
D. Rivard, E. Granger, and R. Sabourin, "Multi-feature extraction and selection in writer-independent off-line signature verification", Int. J. Doc. Anal. Recognit., vol. 16, pp. 83-103, 2013.
I. Bhattacharya, P. Ghosh, and S. Biswas, "Offline signature verification using pixel matching technique", Procedia Technology, vol. 10, pp. 970-977, 2013.
M.A. Ferrer, M. Diaz-Cabrera, and A. Morales, "Static signature synthesis: A neuromotor inspired approach for biometrics", IEEE T. Pattern Anal., vol. 37, pp. 667-680, 2015.
Y.D. Chen, and L.M. Wang, "Face recognition based on convolution neural network", J. Northeast Normal Uni., vol. 48, pp. 70-76, 2016.
G.S. Eskander, R. Sabourin, and E. Granger, "Hybrid writer-independent–writer-dependent offline signature verification system", IET Biometrics, vol. 2, pp. 169-181, 2013.
M.B. Yilmaz, Offline signature verifiation with user-based and global classifiers of local features., Sabancı University, 2015.
J. Hu, and Y. Chen, "Offline signature verification using real adaboost classifier combination of pseudo-dynamic features. In ", Document analysis and recognition (ICDAR), 2013 12th International conference on IEEE, 2013, pp. 1345-1349.
M. Liang, and X. Hu, "Recurrent convolutional neural network for object recognition", Proceedings of the IEEE conference on computer vision and pattern recognition, 2015pp. 3367-3375
J.F. Vargas, C.M. Travieso, and J.B. Alonso, "Off-line signature verification based on gray level information using wavelet transform and texture features In ", Frontiers in handwriting recognition (ICFHR), 2010 International conference on IEEE , 2010 pp. 587-592.
C. Feng, S. Wenkang, and Z. Songwei, Research on off-line handwritten signature recognition technology., Shanghai: Shanghai Jiaotong University:, 2007.
A. Krizhevsky, I. Sutskever and G.E. Hinton., Imagenet classification with deep convolutional neural networks. In .Advances in neural information processing systems 2012, pp. 1097-1105.
X. Glorot, and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks", Aistats, vol. 9, pp. 249-256, 2010.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2019
Page: [348 - 355]
Pages: 8
DOI: 10.2174/1872212112666180925152231
Price: $58

Article Metrics

PDF: 11
PRC: 3