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A Deep Insight into Signature Verification Using Deep Neural Network

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1252))

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Abstract

Signature is an essential biometric modality used in daily life to authenticate one person on his claim. Due to the widespread usage of stamps in office work, banks, forensic documents, etc., it captures the great interest of the researchers for the past many years. With the advancement in the technology and internet, automatic signature verification considered with renewed interest by the researchers. In this paper, we present the state-of-the-art signature verification mechanism and a novel method to perform signature verification in the RGB images. We train our model using advanced deep learning technique (Deep Neural network (DNN)) to differentiate between genuine and forged classes of signatures. The proposed algorithm evaluated on 4NSigComp2010 and 4NSigComp2012 datasets with the same experimental setup. The results showed that the proposed algorithm outperforms the systems presented in the competitions with an EER of 12 and 11.75, respectively.

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Notes

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    http://www.iapr-tc11.org/mediawiki/index.php.

  2. 2.

    http://www.iapr-tc11.org/mediawiki/index.php.

References

  1. Akhtar, N., Shafait, F., Mian, A.: Futuristic greedy approach to sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 53(4), 2157–2174 (2014)

    Google Scholar 

  2. Akhtar, N., Shafait, F., Mian, A.: Bayesian sparse representation for hyperspectral image super resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3631–3640 (2015)

    Google Scholar 

  3. Alahi, A., Ortiz, R., Vandergheynst, P.: Freak: fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–517. IEEE (2012)

    Google Scholar 

  4. Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, pp. 8–15 (2013)

    Google Scholar 

  5. Bajaj, R., Chaudhury, S.: Signature verification using multiple neural classifiers. Pattern Recogn. 30(1), 1–7 (1997)

    Google Scholar 

  6. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Google Scholar 

  7. Blumenstein, M., Armand, S., Muthukkumarasamy, V.: Off-line signature verification using the enhanced modified direction feature and neural based classification. In: International Joint Conference on Neural Networks (2006)

    Google Scholar 

  8. Blumenstein, M., Ferrer, M.A., Vargas, J.F.: The 4NSigComp2010 off-line signature verification competition: scenario 2. In: 2010 12th International Conference on Frontiers in Handwriting Recognition, pp. 721–726. IEEE (2010)

    Google Scholar 

  9. Butt, U.M., Ahmad, S., Shafait, F., Nansen, C., Mian, A.S., Malik, M.I.: Automatic signature segmentation using hyper-spectral imaging. In: 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 19–24. IEEE (2016)

    Google Scholar 

  10. National Research Council: Strengthening forensic science in the United States: a path forward. National Academies Press (2009)

    Google Scholar 

  11. Deng, P.S., Mark Liao, H.-Y., Ho, C.W., Tyan, H.-R.: Wavelet-based off-line handwritten signature verification. Comput. Vis. Image Underst. 76(3), 173–190 (1999)

    Google Scholar 

  12. Di Lecce, V., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., Salzo, A., Sarcinella, L.: Selection of reference signatures for automatic signature verification. In: Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR 1999 (Cat. No. PR00318) (1999)

    Google Scholar 

  13. Dyer, A.G., Found, B., Rogers, D.: Visual attention and expertise for forensic signature analysis. J. Forensic Sci. 51(6), 1397–1404 (2006)

    Google Scholar 

  14. Fadhel, E.A., Bhattacharyya, P.: Application of a steerable wavelet transform using neural network for signature verification. Pattern Anal. Appl. 2(2), 184–195 (1999)

    Google Scholar 

  15. Fang, B., Leung, C.H., Tang, Y.Y., Tse, K.W., Kwok, P.C.K., Wong, Y.K.: Off-line signature verification by the tracking of feature and stroke positions. Pattern Recogn. 36(1), 91–101 (2003)

    MATH  Google Scholar 

  16. Fergani, B., Davy, M., Houacine, A.: Speaker diarization using one-class support vector machines. Speech Commun. 50(5), 355–365 (2008)

    Google Scholar 

  17. Ferrer, M.A., Alonso, J.B., Travieso, C.M.: Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 993–997 (2005)

    Google Scholar 

  18. Gonzalez-Rodriguez, J., Fierrez-Aguilar, J., Ramos-Castro, D., Ortega-Garcia, J.: Bayesian analysis of fingerprint, face and signature evidences with automatic biometric systems. Forensic Sci. Int. 155(2–3), 126–140 (2005)

    Google Scholar 

  19. Huang, K., Yan, H.: Stability and style-variation modeling for on-line signature verification. Pattern Recogn. 36(10), 2253–2270 (2003)

    MATH  Google Scholar 

  20. Huber, R.A., Headrick, A.M.: Handwriting Identification: Facts and Fundamentals. CRC Press, Boca Raton (1999)

    Google Scholar 

  21. Khan, Z., Shafait, F., Mian, A.: Joint group sparse pca for compressed hyperspectral imaging. IEEE Trans. Image Process. 24(12), 4934–4942 (2015)

    MathSciNet  MATH  Google Scholar 

  22. Kovari, B., Charaf, H.: A study on the consistency and significance of local features in off-line signature verification. Pattern Recogn. Lett. 34(3), 247–255 (2013)

    Google Scholar 

  23. Lindblom, B.S., Kelly, J.S.: Organization and content of book. In: Scientific Examination of Questioned Documents, pp. 21–26. CRC Press (2006)

    Google Scholar 

  24. Liwicki, M., Malik, M.I.: Surprising? Power of local features for automated signature verification. In: The 15th International Graphonomics Society Conference (IGS2011), pp. 18–21 (2011)

    Google Scholar 

  25. Liwicki, M., Malik, M.I., Alewijnse, L., van den Heuvel, E., Found, B.: ICFHR 2012 competition on automatic forensic signature verification (4NsigComp 2012). In: 2012 International Conference on Frontiers in Handwriting Recognition, pp. 823–828. IEEE (2012)

    Google Scholar 

  26. Lowe, G.D.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157. IEEE (1999)

    Google Scholar 

  27. Malik, M.I., Ahmed, S., Liwicki, M., Dengel, A.: Freak for real time forensic signature verification. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 971–975. IEEE (2013)

    Google Scholar 

  28. Malik, M.I., Liwicki, M., Dengel, A., Uchida, S., Frinken, V.: Automatic signature stability analysis and verification using local features. In: 2014 14th International Conference on Frontiers in Handwriting Recognition, pp. 621–626. IEEE (2014)

    Google Scholar 

  29. Nguyen, V., Blumenstein, M.: An application of the 2D Gaussian filter for enhancing feature extraction in off-line signature verification. In: 2011 International Conference on Document Analysis and Recognition, pp. 339–343. IEEE (2011)

    Google Scholar 

  30. Nyssen, E., Sahli, H., Zhang, K.: A multi-stage online signature verification system. Pattern Anal. Appl. 5(3), 288–295 (2002)

    MathSciNet  Google Scholar 

  31. Pirlo, G., Impedovo, D.: Cosine similarity for analysis and verification of static signatures. IET Biom. 2(4), 151–158 (2013)

    Google Scholar 

  32. Plamondon, R., Srihari, S.N.: Online and off-line handwriting recognition: a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 63–84 (2000)

    Google Scholar 

  33. Ramesh, V.E., Murty, M.N.: Off-line signature verification using genetically optimized weighted features. Pattern Recogn. 32(2), 217–233 (1999)

    Google Scholar 

  34. Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), vol. 2, pp. 1508–1515. IEEE (2005)

    Google Scholar 

  35. Stoker, W.H.: Questioned documents. In: Introduction to Forensic Sciences, p. 133 (1996)

    Google Scholar 

  36. Viswanathan, D.G.: Features from accelerated segment test (FAST). Inf. Ed. Ac. Uk (2009)

    Google Scholar 

  37. Wijesoma, W.S., Yue, K.W., Chien, K.L., Chow, T.K.: Online handwritten signature verification for electronic commerce over the Internet. In: Asia-Pacific Conference on Web Intelligence, pp. 227–236. Springer (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University Sains Malaysia, Gelugor, Malaysia

    Umair Muneer Butt

  2. University of Lahore chenab Campus, Gujrat, Pakistan

    Umair Muneer Butt, Fatima Masood, Zaib unnisa, Zaheer Dar, Samreen Azhar & Irfan Abbas

  3. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Shahid Razzaq

  4. Minhaj University, Lahore, Pakistan

    Munib Ahmad

Authors
  1. Umair Muneer Butt
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  2. Fatima Masood
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  3. Zaib unnisa
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  4. Shahid Razzaq
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  5. Zaheer Dar
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  6. Samreen Azhar
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  7. Irfan Abbas
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  8. Munib Ahmad
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Corresponding author

Correspondence to Umair Muneer Butt .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Butt, U.M. et al. (2021). A Deep Insight into Signature Verification Using Deep Neural Network. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_10

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