CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification

Wifda Muna Fatihia - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Arna Fariza - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia
Tita Karlita - Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia


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DOI: http://dx.doi.org/10.30630/joiv.7.1.1443

Abstract


Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality.

 


Keywords


Handwritten Signature; Signature Verification; Batch Normalization; Convolutional Neural Network

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References


C. Wencheng, G. Xiaopeng, S. Hong, and Z. Limin, “Offline Chinese signature verification based on AlexNet,†in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2018, vol. 219, pp. 33–37, doi: 10.1007/978-3-319-73317-3_5.

A. Sanjoy, P. Soumya, R. Nayak, and T. Hanne, “Off-line signature verification using elementary combinations of directional codes from boundary pixels,†Neural Comput. Appl., vol. 6, 2021, doi: 10.1007/s00521-021-05854-6.

F. E. Batool et al., “Offline signature verification system: a novel technique of fusion of GLCM and geometric features using SVM,†Multimed. Tools Appl., 2020, doi: 10.1007/s11042-020-08851-4.

G. Suhas, S. Chiranjeevi, S. S. Mokshagundam, and S. Suraj, “SIFR-signature fraud recognition,†in 2018 International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2018 - Proceedings, 2018, pp. 1–6, doi: 10.1109/ICNEWS.2018.8903995.

S. N. Srihari, H. Srinivasan, S. Chen, and M. J. Beal, “Machine learning for signature verification,†Stud. Comput. Intell., vol. 90, pp. 387–408, 2008, doi: 10.1007/978-3-540-76280-5_15.

M. M. Yapıcı, A. Tekerek, and N. Topaloğlu, “Deep learning-based data augmentation method and signature verification system for offline handwritten signature,†Pattern Anal. Appl., vol. 24, no. 1, pp. 165–179, 2021, doi: 10.1007/s10044-020-00912-6.

M. M. Yapici, A. Tekerek, and N. Topaloglu, “Convolutional Neural Network Based Offline Signature Verification Application,†Int. Congr. Big Data, Deep Learn. Fight. Cyber Terror. IBIGDELFT 2018 - Proc., no. December, pp. 30–34, 2019, doi: 10.1109/IBIGDELFT.2018.8625290.

A. Dutta, U. Pal, and J. Llados, “Compact correlated features for writer independent signature verification,†in Proceedings - International Conference on Pattern Recognition, 2016, pp. 3422–3427, doi: 10.1109/ICPR.2016.7900163.

L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Meta-learning for fast classifier adaptation to new users of Signature Verification systems,†IEEE Trans. Inf. Forensics Secur., vol. PP, no. c, p. 1, 2019, doi: 10.1109/TIFS.2019.2949425.

P. Singh, P. Verma, and N. Singh, “Offline Signature Verification : An Application of GLCM Features in Machine Learning,†Ann. Data Sci., no. 0123456789, 2021, doi: 10.1007/s40745-021-00343-y.

S. Jerome Gideon, A. Kandulna, A. A. Kujur, A. Diana, and K. Raimond, “Handwritten Signature Forgery Detection using Convolutional Neural Networks,†Procedia Comput. Sci., vol. 143, pp. 978–987, 2018, doi: 10.1016/j.procs.2018.10.336.

H. Bunke, J. Csirik, Z. Gingl, and E. Griechisch, “Online signature verification method based on the acceleration signals of handwriting samples,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 7042 LNCS, pp. 499–506, doi: 10.1007/978-3-642-25085-9_59.

T. Younesian, S. Masoudnia, R. Hosseini, and B. N. Araabi, “Active Transfer Learning for Persian Offline Signature Verification,†4th Int. Conf. Pattern Recognit. Image Anal. IPRIA 2019, pp. 234–239, 2019, doi: 10.1109/PRIA.2019.8786013.

M. M. Hameed, R. Ahmad, M. L. M. Kiah, and G. Murtaza, “Machine Learning-based Offline Signature Verification Systems: A Systematic Review,†Signal Process. Image Commun., vol. 93, no. October 2020, p. 116139, 2021, doi: 10.1016/j.image.2021.116139.

E. Guerra-Segura, A. Ortega-Pérez, and C. M. Travieso, “In-air signature verification system using Leap Motion,†Expert Syst. Appl., vol. 165, no. August 2020, 2021, doi: 10.1016/j.eswa.2020.113797.

D. Impedovo and G. Pirlo, “Automatic signature verification: The state of the art,†IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 38, no. 5, pp. 609–635, 2008, doi: 10.1109/TSMCC.2008.923866.

S. C. Satapathy, A. Govardhan, K. Srujan Raju, and J. K. Mandal, “Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1,†in Advances in Intelligent Systems and Computing, 2015, vol. 337, pp. 337–338, doi: 10.1007/978-3-319-13728-5.

C. Ishikawa, J. A. U. Marasigan, and M. V. C. Caya, “Cloud-based Signature Validation Using CNN Inception-ResNet Architecture,†2020, doi: 10.1109/HNICEM51456.2020.9400027.

M. Hanmandlu, A. B. Sronothara, and S. Vasikarla, “Deep Learning based Offline Signature Verification,†in 2018 9th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2018, 2018, pp. 732–737, doi: 10.1109/UEMCON.2018.8796678.

S. J. Lee, T. Chen, L. Yu, and C. H. Lai, “Image Classification Based on the Boost Convolutional Neural Network,†IEEE Access, vol. 6, no. c, pp. 12755–12768, 2018, doi: 10.1109/ACCESS.2018.2796722.

M. Hussain, J. J. Bird, and D. R. Faria, “A study on CNN transfer learning for image classification,†Adv. Intell. Syst. Comput., vol. 840, pp. 191–202, 2019, doi: 10.1007/978-3-319-97982-3_16.

L. Liu, L. Huang, F. Yin, and Y. Chen, “Offline signature verification using a region based deep metric learning network,†Pattern Recognit., vol. 118, p. 108009, 2021, doi: 10.1016/j.patcog.2021.108009.

N. Sharma, V. Jain, and A. Mishra, “An Analysis of Convolutional Neural Networks for Image Classification,†Procedia Comput. Sci., vol. 132, no. Iccids, pp. 377–384, 2018, doi: 10.1016/j.procs.2018.05.198.

S. Alkaabi, S. Yussof, S. Almulla, H. Al-Khateeb, and A. A. Alabdulsalam, “A novel architecture to verify offline hand-written signatures using convolutional neural network,†in 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019, 2019, pp. 0–3, doi: 10.1109/3ICT.2019.8910275.

S. Loussaief and A. Abdelkrim, “Deep learning vs. bag of features in machine learning for image classification,†2018 Int. Conf. Adv. Syst. Electr. Technol. IC_ASET 2018, pp. 6–10, 2018, doi: 10.1109/ASET.2018.8379825.

M. T. F. Rabbi et al., “Handwritten Signature Verification Using CNN with Data Augmentation,†J. Contents Comput., vol. 1, no. 1, pp. 25–37, 2019, doi: 10.9728/jcc.2019.12.1.1.25.

D. Avola, M. J. Bigdello, L. Cinque, A. Fagioli, and M. R. Marini, “R-SigNet : Re duce d space writer-independent feature learning for offline writer-dependent signature verification,†Pattern Recognit. Lett., vol. 150, pp. 189–196, 2021, doi: 10.1016/j.patrec.2021.06.033.

C. S. Vorugunti, R. K. S. Gorthi, and V. Pulabaigari, “Online signature verification by few-shot separable convolution based deep learning,†in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 2019, pp. 1125–1130, doi: 10.1109/ICDAR.2019.00182.

D. Justus, J. Brennan, S. Bonner, and A. S. McGough, “Predicting the Computational Cost of Deep Learning Models,†Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018, pp. 3873–3882, 2019, doi: 10.1109/BigData.2018.8622396.

Kaiming He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,†IEEE, 2015, [Online]. Available: http://image-net.org/challenges/LSVRC/2015/.

H. Yong, J. Huang, D. Meng, X. Hua, and L. Zhang, “Momentum Batch Normalization for Deep Learning with Small Batch Size,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 12357 LNCS, pp. 224–240, 2020, doi: 10.1007/978-3-030-58610-2_14.

L. N. Wang, G. Zhong, S. Yan, J. Dong, and K. Huang, Enhanced LSTM with batch normalization, vol. 11953 LNCS. 2019.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,†32nd Int. Conf. Mach. Learn. ICML 2015, vol. 1, pp. 448–456, 2015.

Y. Wu and K. He, Group Normalization, vol. 128, no. 3. Springer International Publishing, 2020.

L. Liu, L. Huang, F. Yin, and Y. Chen, “Offline Signature Verification using a Region Based Deep Metric Learning Network,†Pattern Recognit., vol. 118, p. 108009, 2021, doi: 10.1016/j.patcog.2021.108009.

D. Banerjee, B. Chatterjee, P. Bhowal, T. Bhattacharyya, S. Malakar, and R. Sarkar, “A new wrapper feature selection method for language-invariant offline signature verification,†Expert Syst. Appl., vol. 186, no. March, 2021, doi: 10.1016/j.eswa.2021.115756.

S. Dey, A. Dutta, J. I. Toledo, S. K. Ghosh, J. Llados, and U. Pal, “SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification,†Elsevier, no. 1, pp. 1–7, 2017, [Online]. Available: http://arxiv.org/abs/1707.02131.

M. Hron and N. Obwegeser, “Scrum in Practice: An Overview of Scrum Adaptations,†Proc. Annu. Hawaii Int. Conf. Syst. Sci., vol. 2018-Janua, pp. 5445–5454, 2018, doi: 10.24251/hicss.2018.679.

J. F. Vargas, M. A. Ferrer, C. M. Travieso, and J. B. Alonso, “Off-line signature verification based on grey level information using texture features,†Pattern Recognit., vol. 44, no. 2, pp. 375–385, 2011, doi: 10.1016/j.patcog.2010.07.028.

S. M. A. Navid, S. H. Priya, N. H. Khandakar, Z. Ferdous, and A. B. Haque, “Signature Verification Using Convolutional Neural Network,†2019 IEEE Int. Conf. Robot. Autom. Artif. Internet-of-Things, RAAICON 2019, vol. 16, no. 1, pp. 35–39, 2019, doi: 10.1109/RAAICON48939.2019.19.

D. D. Franceschi and J. H. Jang, “Demystifying batch normalization: Analysis of normalizing layer inputs in neural networks,†Commun. Comput. Inf. Sci., vol. 1173 CCIS, pp. 49–57, 2020, doi: 10.1007/978-3-030-41913-4_5.

Z. Q. Zhao, H. Bian, D. Hu, W. Cheng, and H. Glotin, “Pedestrian detection based on fast R-CNN and batch normalization,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10361 LNCS, pp. 735–746, 2017, doi: 10.1007/978-3-319-63309-1_65.

A. Foroozandeh, A. Askari Hemmat, and H. Rabbani, “Offline Handwritten Signature Verification and Recognition Based on Deep Transfer Learning,†in Iranian Conference on Machine Vision and Image Processing, MVIP, 2020, vol. 2020-Janua, doi: 10.1109/MVIP49855.2020.9187481.