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Transformation technique for derivation of similarity scores for signatures

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Abstract

The human signature is a widely acceptable biometric characteristic that provides a very secure means for authorizing legal documents. However, signature-based authorization is characterized by problems of counterfeiting and forgery. Hence, the need for adequate verification and protection of signatures. On this note, this paper presents a signature similarity score model that is based on the transformation technique. The model comprises stages for pre-processing, feature extraction, and matching. The pre-processing stage performs Gabor and median filtering, normalization, rescaling, histogram equalization, binarization, and thinning. Gabor and Fourier’s transforms were used for feature extraction and Principal Component Analysis (PCA) for dimensionality reduction towards preventing information loss. The spatial frequency domain property of the input textures was extracted to form the texture features, while Fourier transform was applied to the texture features to obtain their rotation-invariant version. Finally, Euclidean distance-based feature analysis was carried out. Analysis of results from the experimental study of the model on the Netherlands Forensic Institute (NFI) standard signature dataset revealed that with the free or minimal noise level, the algorithm performed well. It was also established that each stage of the enhancement process is important for performance optimization. The comparison of experimental results with what was obtained for some similar and recent models showed an encouraging and superior performance level of the proposed model.

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References

  1. Kumar, D.: A novel bank check signature verification model using concentric circle masking features and its performance analysis over various neural network training functions. Indian J. Sci. Technol. 9(31) (2016)

  2. Teoh, A.B.J., Leng, L.: Special issue on advanced biometrics with deep learning. Appl. Sci. 10, 4453 (2020). https://doi.org/10.3390/app10134453

    Article  Google Scholar 

  3. Fanga, B., Leung, B.C., Tang, Y.: Online signature verification by the tracking of feature and stroke positions, Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong (2003)

  4. Ajij, M., Pratihar, S., Nayak, S.R., Hanne, T., Roy, D.S.: Off-line signature verification using elementary combinations of directional codes from boundary pixels. Neural Comput. Appl. (2021)

  5. Luiz, G., Robert, S., Luiz, S.: Learning features for offline handwritten signature verification using deep convolutional neural networks. Pattern Recogn. 70, 163–176 (2017)

    Article  Google Scholar 

  6. Supinder, S., Amandeep, K.: Off-line signature verification using sub uniform local binary patterns and support vector machine. In: International Conference on Chemical Engineering and Advanced Computational Technologies, Nov. 24–25, Pretoria (South Africa) (2014).

  7. Biometrics and Forensics Ethics Group (BFEG, 2021) Annual Report 2020/21. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1002555/BFEG_annual_report_2020_-_2021.pdf

  8. Indrajit, B., Prabir, G., Swarup, B.: Offline Signature verification using pixel matching technique. Procedia Technol. 10, 970–977 (2013)

    Article  Google Scholar 

  9. Pazarbasioglu, C., Mora, A.G., Uttamchandani, M., Natarajan, H., Feyen, E., Saal, M.: Digital Financial Services. World Bank Group, Digital-Financial-Services.pdf (worldbank.org) (2020)

  10. Pushpalatha, K., Gautham, A., Shashikumar, D., Das, R.: Offline signature verification with random and skilled forgery detection using polar domain features and multi-stage classification-regression model. Int. J. Adv. Sci. Technol. 59, 27–40 (2013)

    Article  Google Scholar 

  11. Vahid, K., Reza, P., Hamid, R.: Offline signature verification using local random transform and support vector machines‖. Int. J. Image Process. (IJIP) 3(5), 184–194 (2010)

    Google Scholar 

  12. Venkataramu, A.C., Masahiko, A., Akshaya, A., Gurupura, A.P., Kumar, U., Rajashekar, R.R.: Offline signature recognition and verification using ORB key point matching techniques. Adv. Sci. Technol. Eng. Syst. J. 5(4), 01–07 (2020)

    Article  Google Scholar 

  13. Afrianto, I., Heryandi, A., Finandhita, A., Atin, S.: E-document authentification with digital signature for smart city: reference model. In: The 2nd ASEAN Workshop on Information Science and Technology (AWIST2019), Universitas Komputer Indonesia, Bandung (2019). https://www.researchgate.net/publication/334736514_E-Document_Autentification_With_Digital_Signature_For_Smart_City_Reference_Model

  14. Hamidur, R., Pial, R.: Digital Signature Understanding. How it Works and Importance (2020). https://www.researchgate.net/publication/344818547_Digital_Signature_Understanding_How_it_Works_and_Importance

  15. Meetu, S., Daulat, S.: Handwritten signature recognition, verification and dynamic updation using neural network. Int. J. Adv. Res. Comput. Commun. Eng 4(8) (2015)

  16. Poddar, J., Parikh, V., Bharti, S.K.: Offline Signature Recognition and Forgery Detection using Deep Learning. Proc. Comput. Sci. 170, 610–617 (2020)

    Article  Google Scholar 

  17. Best Practice Manual for the Forensic Examination of Handwriting (BPM, 2020), https://enfsi.eu/wp-content/uploads/2021/01/BPM-Handwriting-%E2%80%94-Edition-3.pdf

  18. Abdalla, A., Zhirkov, V.: Offline signature verification using random transform and SVM/KNN classifiers. TSTU Trans. 15(1), 62–69 (2009)

    Google Scholar 

  19. Rabia, V., Amit, P.: A Review paper on offline signature recognition system using Random transform, Genetic algorithm and Neural network. Int. J. Eng. Sci. Res. Technol: 5–8 (2016).

  20. Huang, D., Gao, J.: On-line signature verification based on GA-SVM (2015)

  21. Ashok, D., Dhandapani, S.: A novel bank check signature verification model using concentric circle masking features and its performance analysis over various neural network training functions. Indian J. Sci. Technol. 9(1) (2016)

  22. Sadia, A., Navjot, K., Bansal, P.: Signature verification technique using artificial neural network and SURF algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 5(6) (2016)

  23. Jadhav, T.: Handwritten signature verification using local binary pattern features and KNN. Int. Res. J. Eng. Technol. 6(7) (2019)

  24. Harsha, M., Chavan, G., Pradnya, A.: (2018) Offline handwritten signature recognition system. A behavioral biometric paperback. International Kindle Paperwhite

  25. Iwasokun, G.B., Opatoye, K.I., Orunmuyi, B.O.: Multi-modal biometrics fusion based on component analysis and stationery wavelet. Transf. Int. J. Inform. Secur. Sci. 9(2), 114–125 (2020)

    Google Scholar 

  26. Coetzer, J., Herbst, B., Preez, J.: Offline signature verification using the discrete random transform and a hidden markov model. EURASIP J. Appl. Signal Process: 559–571 (2014)

  27. Jasmeet, S., Reecha, K.: An analytical analysis on signature recognition and verification is presented. Int. J. Comput. Eng. Res. 7(7) (2017)

  28. Sigari, M., Reza, P., Mohamad, H., Reza, P.: (2011) Offline handwritten signature identification and verification using multi-resolution gabor wavelet. Federal University of Mashhad, Iran. Int. J. Biomet. Bioinform. (IJBB) 5(4).

  29. Suvarnsing, G.: (2015) A review paper on multimodal biometrics system using fingerprint and signature. IJCA 128(15).

  30. Hadeel, S., Nada, M.: An offline signature recognition using discrete random transform and neural network is presented. Int. J. Comput. Sci. Mob. Comput 4(10) (2015)

  31. Sobia, J., Anil, J.: Offline signature recognition system using global feature, ACO and neural network. Int. J. Adv. Comput. Manag. Stud. (IJACMS) 1(6), 16–22 (2016)

    Google Scholar 

  32. Ali, K., Bassam, R., Sania, B.: Offline signature recognition using neural networks approach. Proc. Comput. Sci. 3, 151–161 (2010)

    Google Scholar 

  33. Farhan, H.R., Kod, M.S., Shahadi, H.I.: A wireless multi-access security system using real-time face recognition technique. J. Eng. Sci. Technol. 15(5), 2890–2905 (2020)

    Google Scholar 

  34. Rohilla, S., Sharma, A., Singla, R.K.: Role of sub-trajectories in online signature verification. Array 6 (2020)

  35. Hazem, H., Alomari, R.S., Kobbaey, T., Al-Khatib, R.Z.: Off-line signature verification system based on DWT and common features extraction. J. Theor. Appl. Inform. Technol. 51(2), 165–174 (2022)

    Google Scholar 

  36. Marco, D., Camilleri, P.: Handwritten signature verification by independent component analysis department of computer science, St. Martins Institute of IT (2011)

  37. Yuan, S., Abe, M., Taguchi, A., Kawamata, M.: High accuracy bicubic interpolation using image local features. Fund IEICE Trans E90–A(8) (2007)

  38. Deepali, H., Tejas, V.: Signature recognition and verification: The most acceptable biometrics for security. Int. J. Appl. Innov. Eng. Manag. 4(8), 4–6 (2015)

    Google Scholar 

  39. Armand, S., Blumenstein, M., Muthukkumarasamy, V.: Off-line signature verification using the enhanced modified direction feature and neural-based classification. In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 684–691 (2006)

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The research did not enjoy any external funding.

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Authors and Affiliations

  1. Department of Computer Science, Federal University of Technology, Akure, Nigeria

    Joel Adeyanju Adewuyi

  2. Department of Software Engineering, Federal University of Technology, Akure, Nigeria

    Gabriel Babatunde Iwasokun

  3. Department of Cyber Security, Federal University of Technology, Akure, Nigeria

    Arome Junior Gabriel

Authors
  1. Joel Adeyanju Adewuyi
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  2. Gabriel Babatunde Iwasokun
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  3. Arome Junior Gabriel
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Contributions

All authors contributed to the conception and design of the research. Material preparation, data collection, and analysis were performed by Joel Adeyanju Adewuyi. The first draft of the manuscript was written by Gabriel Babatunde Iwasokun and Arome Junior Gabriel. All the authors read and approved the final manuscript and also agreed to all the content of the article including the author list and contributions.

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Correspondence to Gabriel Babatunde Iwasokun.

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Adewuyi, J.A., Iwasokun, G.B. & Gabriel, A.J. Transformation technique for derivation of similarity scores for signatures. Iran J Comput Sci 5, 317–328 (2022). https://doi.org/10.1007/s42044-022-00113-w

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  • DOI: https://doi.org/10.1007/s42044-022-00113-w

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