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Handwritten signature verification using shallow convolutional neural network

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Abstract

Handwritten signatures are an undeniable and unique way to prove the identity of persons. Owing to the simplicity and uniqueness, it finds an essential place in the area of behavioral biometric. Signatures are the most widely accepted biometric trait by law enforcement agencies/personnel for verification purposes, especially in financial institutions, legal transactions, etc. and hence secured authentication becomes imperative. In the era of a digital age, numerous transactions are taking place, where handwritten signature verification is required by the agencies, e.g., banks, etc. In such scenarios, the process of signature verification, besides being accurate and secure, should be very fast, i.e., the real-time verification can be done. In this paper, we have proposed a convolutional neural network-based language-independent shallow architecture (sCNN(Shallow Convolutional Neural Network)) for signature verification. The proposed architecture is very simple but extremely efficient in terms of accuracy. A custom shallow convolution neural network is used to automatically learn the features of signature from the provided training data. Another contribution of the research work, which is the handwritten signature data collection for 137 subjects and 467 subjects, which are named as CVBLSig-V1 and CVBLSig-V2 respectively, has been reported in this paper. The performance of the proposed architecture has been evaluated on publicly available datasets, i.e., MCYT-75, MCYT-100, and GPDS, as well as CVBLSig-V1 and CVBLSig-V2. The performance was also compared with state of the art reported methods and shown improved, while considering the accuracy and equal error rate (EER) as performance metrics.

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a. Change in Aspect ratio b. Rotation by 30 degrees in clockwise direction c. Right Shear d. Rotation by 10 degrees clockwise e. Left Shear f. Rotation by 30 degrees in anticlockwise direction g. Gaussian Noise h. Rotation by 10 degrees anticlockwise

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Notes

  1. http://atvs.ii.uam.es/atvs/mcyt100s.html

  2. http://atvs.ii.uam.es/atvs/mcyt75so.html

  3. https://drive.google.com/file/d/0B29vNACcjvzVc1RfVkg5dUh2b1E/view

  4. http://www.gpds.ulpgc.es/downloadnew/download.htm

  5. https://www.cse.ust.hk/svc2004/download.html

  6. https://cvbl.iiita.ac.in/dataset.php

  7. anamika06jain@gmail.com, rsi2016005@iiita.ac.in

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Acknowledgments

This research is conducted in Indian Institute of Information Technology Allahabad and supported by the Ministry of Human Resource and Development, Government of India. We are grateful to the support of NVIDIA Corporation. NVIDIA Corporation has donated TITANX(PASCAL) GPU with 3584 CUDA cores.

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  1. Indian Institute of Information Technology Allahabad, Uttar Pradesh, India

    Anamika Jain, Satish Kumar Singh & Krishna Pratap Singh

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  1. Anamika Jain
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Correspondence to Anamika Jain.

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Appendices

Appendix

A Feature Maps and Filters after each Convolutional Layer

In Figs. 3 and 4 we have shown only one filter and one activation map of each convolutional layer. The detailed features and filter from each convolutional layers has been shown in Figs. 17, 18, 19, 20, 21 and 22.

Fig. 17
figure 17

Visualization of all the feature maps after first convolution layer

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Fig. 18
figure 18

Visualization of all the feature maps after second the convolution layer

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Fig. 19
figure 19

Visualization of all the feature maps after third convolution layer

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Fig. 20
figure 20

Visualization of all the Filters of first convolution layer

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Fig. 21
figure 21

Visualization of all the Filters of second convolution layer

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Fig. 22
figure 22

Visualization of all the Filters of third convolution layer

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Jain, A., Singh, S.K. & Singh, K.P. Handwritten signature verification using shallow convolutional neural network. Multimed Tools Appl 79, 19993–20018 (2020). https://doi.org/10.1007/s11042-020-08728-6

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