Contactless Biometric Fingerprint and Signature Identification and Verification

Authors(5) :-Dr. D. Thilagavathy, N. Deepasri, P. Gahana, S. Nivethasree, M. Varshini

To query the biometric and fingerprint and signature identification, the database communicates with a single secure server as if the entire database is stored in it. In CSP, outsourced encrypted biometric and fingerprint and signature identification are stored in a distributed manner, whereas the secure server manages the query processing on such a distributed database. The desired data will be distributed and stored in secure servers which increases the verification of accessing data. It stores data in a particular cloud server from which the server distributes them based on availability and performance. It increases the security of the verification process by comparing the details stored. Study on signature and finger knuckle patterns has attracted increasing attention for the automated biometric signature identification. Signature and finger knuckle pattern is essentially a biometric identifier and the usage or availability of 2D knuckles and key limitations to avail biometric identifiers. So the proposal since proposes 2D signature and finger knuckle collection of data, which was gathered from various sources using a photometric imaging stereo approach. This paper investigates on 2D information from signature and finger knuckle patterns and introduces a new feature descriptor to extract discriminative features for accurate signature and finger knuckle matching. An individuality model for the proposed feature descriptor is also presented. Expected experimental analysis by consuming state of the art technology on the signature and finger pattern executes and validates the efficiency of the proposal. This process is verified from the obtained results, using the state of the art technique, signature and fingerprint collection patterns on available datasets that are distributed.

Authors and Affiliations

Dr. D. Thilagavathy
Professor, Department of IT, Adhiyamaan College of Engineering (Autonomous), Dr. M. G. R. Nagar, Hosur, Tamil Nadu, India
N. Deepasri
UG Student, Department of IT, Adhiyamaan College of Engineering (Autonomous), Dr. M. G. R. Nagar, Hosur, Tamil Nadu, India
P. Gahana
UG Student, Department of IT, Adhiyamaan College of Engineering (Autonomous), Dr. M. G. R. Nagar, Hosur, Tamil Nadu, India
S. Nivethasree
UG Student, Department of IT, Adhiyamaan College of Engineering (Autonomous), Dr. M. G. R. Nagar, Hosur, Tamil Nadu, India
M. Varshini
UG Student, Department of IT, Adhiyamaan College of Engineering (Autonomous), Dr. M. G. R. Nagar, Hosur, Tamil Nadu, India

Service Oriented Architecture (SOA), State-of- the-Art, Support Vector Machine (SVM), Deep Neural Network (DNN), Knuckle Patterns.

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Publication Details

Published in : Volume 5 | Issue 5 | March-April 2020
Date of Publication : 2020-03-05
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 189-197
Manuscript Number : EBHIT027
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Dr. D. Thilagavathy, N. Deepasri, P. Gahana, S. Nivethasree, M. Varshini, " Contactless Biometric Fingerprint and Signature Identification and Verification", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 5, Issue 5, pp.189-197, March-April-2020.
Journal URL : https://ijsrst.com/EBHIT027
Citation Detection and Elimination     |      | |
  • “Information Technology - Biometric Data Interchange Formats - Part 7: Signature/Sign Time Series Data,” ISO/IEC Standard 19794-7:2014, ISO/IEC JTC1, 2014.
  • "Information Technology - Biometric Performance Testing and Reporting - Part 3: modality-specific Testing," ISO/IEC TR Standard 19795- 3:2007, ISO/IEC JTC1, 2007.
  • “Information Technology - Biometric Presentation Attack Detection - Part 3: Testing and Reporting,” ISO/IEC Standard 30107-3, ISO/IEC JTC1, 2017
  • G. Dyer, B. Found, and D. Rogers, “Best practice manual for the forensic examination of handwriting,” European Network of Forensic Science Institutes (ENFSI), pp. 1–40, 2015
  • J. E. Douglas and C. Munn, “Modus operandi and the signature aspects of violent crime,” in Crime classification manual, 1992, pp. 259–268.
  • S. A. Slyter, Forensic signature examination. Charles C Thomas Publisher, 1995
  • G. Dyer, B. Found, and D. Rogers, “Visual attention and expertise for forensic signature analysis,” Journal of Forensic Sciences, vol. 51, no. 6, pp. 1397–1404, 2006
  • M. Diaz, M. A. Ferrer, D. Impedovo, M. I. Malik, G. Pirlo, and R. Plamondon, "A prospective analysis of handwritten signature technology," ACM Computing Surveys (CSUR), vol. 51, no. 6, pp. 1–39, 2019
  • L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Offline handwritten signature verification - literature review,” in Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Nov 2017, pp. 1–8.
  • M. I. Malik, M. Liwicki, L. Alewijnse, W. Ohyama, M. Blumenstein, and B. Found, "Signature Verification and Writer Identification Competitions for On- and Offline Skilled Forgeries (SigWiComp2013)," in 12th Int. Conf. on Document Analysis and Recognition, 2013, pp. 1477–1483.
  • M. I. Malik, S. Ahmed, A. Marcelli, U. Pal et al., “ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015),” in Int. Conf. on Document Analysis and Recognition (ICDAR), 2015, pp. 1186–1190.
  • H. Suwanwiwat et al., “Competition on thai student signatures and name components recognition and verification (TSNCRV2018),” in 16th Int. Conf. on Frontiers in Handwriting Recog. (ICFHR), 2018, pp. 500–505.
  • S.-H. Cha and S. N. Srihari, “Writer identification: Statistical analysis and dichotomizer,” in Advances in Pattern Recognition, F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil, Eds. Springer Berlin Heidelberg, 2000, pp. 123-132.
  • S. N. Srihari, Aihua Xu, and M. K. Kalera, “Learning strategies and classification methods for off-line signature verification,” in 9th Int. Workshop on Frontiers in Handwriting Recog., Oct 2004, pp. 161–166.
  • " target="_blank"> BibTeX
    |
  • “Information Technology - Biometric Data Interchange Formats - Part 7: Signature/Sign Time Series Data,” ISO/IEC Standard 19794-7:2014, ISO/IEC JTC1, 2014.
  • "Information Technology - Biometric Performance Testing and Reporting - Part 3: modality-specific Testing," ISO/IEC TR Standard 19795- 3:2007, ISO/IEC JTC1, 2007.
  • “Information Technology - Biometric Presentation Attack Detection - Part 3: Testing and Reporting,” ISO/IEC Standard 30107-3, ISO/IEC JTC1, 2017
  • G. Dyer, B. Found, and D. Rogers, “Best practice manual for the forensic examination of handwriting,” European Network of Forensic Science Institutes (ENFSI), pp. 1–40, 2015
  • J. E. Douglas and C. Munn, “Modus operandi and the signature aspects of violent crime,” in Crime classification manual, 1992, pp. 259–268.
  • S. A. Slyter, Forensic signature examination. Charles C Thomas Publisher, 1995
  • G. Dyer, B. Found, and D. Rogers, “Visual attention and expertise for forensic signature analysis,” Journal of Forensic Sciences, vol. 51, no. 6, pp. 1397–1404, 2006
  • M. Diaz, M. A. Ferrer, D. Impedovo, M. I. Malik, G. Pirlo, and R. Plamondon, "A prospective analysis of handwritten signature technology," ACM Computing Surveys (CSUR), vol. 51, no. 6, pp. 1–39, 2019
  • L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Offline handwritten signature verification - literature review,” in Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Nov 2017, pp. 1–8.
  • M. I. Malik, M. Liwicki, L. Alewijnse, W. Ohyama, M. Blumenstein, and B. Found, "Signature Verification and Writer Identification Competitions for On- and Offline Skilled Forgeries (SigWiComp2013)," in 12th Int. Conf. on Document Analysis and Recognition, 2013, pp. 1477–1483.
  • M. I. Malik, S. Ahmed, A. Marcelli, U. Pal et al., “ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015),” in Int. Conf. on Document Analysis and Recognition (ICDAR), 2015, pp. 1186–1190.
  • H. Suwanwiwat et al., “Competition on thai student signatures and name components recognition and verification (TSNCRV2018),” in 16th Int. Conf. on Frontiers in Handwriting Recog. (ICFHR), 2018, pp. 500–505.
  • S.-H. Cha and S. N. Srihari, “Writer identification: Statistical analysis and dichotomizer,” in Advances in Pattern Recognition, F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil, Eds. Springer Berlin Heidelberg, 2000, pp. 123-132.
  • S. N. Srihari, Aihua Xu, and M. K. Kalera, “Learning strategies and classification methods for off-line signature verification,” in 9th Int. Workshop on Frontiers in Handwriting Recog., Oct 2004, pp. 161–166.
  • " target="_blank">RIS
    |
  • “Information Technology - Biometric Data Interchange Formats - Part 7: Signature/Sign Time Series Data,” ISO/IEC Standard 19794-7:2014, ISO/IEC JTC1, 2014.
  • "Information Technology - Biometric Performance Testing and Reporting - Part 3: modality-specific Testing," ISO/IEC TR Standard 19795- 3:2007, ISO/IEC JTC1, 2007.
  • “Information Technology - Biometric Presentation Attack Detection - Part 3: Testing and Reporting,” ISO/IEC Standard 30107-3, ISO/IEC JTC1, 2017
  • G. Dyer, B. Found, and D. Rogers, “Best practice manual for the forensic examination of handwriting,” European Network of Forensic Science Institutes (ENFSI), pp. 1–40, 2015
  • J. E. Douglas and C. Munn, “Modus operandi and the signature aspects of violent crime,” in Crime classification manual, 1992, pp. 259–268.
  • S. A. Slyter, Forensic signature examination. Charles C Thomas Publisher, 1995
  • G. Dyer, B. Found, and D. Rogers, “Visual attention and expertise for forensic signature analysis,” Journal of Forensic Sciences, vol. 51, no. 6, pp. 1397–1404, 2006
  • M. Diaz, M. A. Ferrer, D. Impedovo, M. I. Malik, G. Pirlo, and R. Plamondon, "A prospective analysis of handwritten signature technology," ACM Computing Surveys (CSUR), vol. 51, no. 6, pp. 1–39, 2019
  • L. G. Hafemann, R. Sabourin, and L. S. Oliveira, “Offline handwritten signature verification - literature review,” in Seventh Int. Conf. on Image Processing Theory, Tools and Applications (IPTA), Nov 2017, pp. 1–8.
  • M. I. Malik, M. Liwicki, L. Alewijnse, W. Ohyama, M. Blumenstein, and B. Found, "Signature Verification and Writer Identification Competitions for On- and Offline Skilled Forgeries (SigWiComp2013)," in 12th Int. Conf. on Document Analysis and Recognition, 2013, pp. 1477–1483.
  • M. I. Malik, S. Ahmed, A. Marcelli, U. Pal et al., “ICDAR2015 competition on signature verification and writer identification for on- and off-line skilled forgeries (SigWIcomp2015),” in Int. Conf. on Document Analysis and Recognition (ICDAR), 2015, pp. 1186–1190.
  • H. Suwanwiwat et al., “Competition on thai student signatures and name components recognition and verification (TSNCRV2018),” in 16th Int. Conf. on Frontiers in Handwriting Recog. (ICFHR), 2018, pp. 500–505.
  • S.-H. Cha and S. N. Srihari, “Writer identification: Statistical analysis and dichotomizer,” in Advances in Pattern Recognition, F. J. Ferri, J. M. Inesta, A. Amin, and P. Pudil, Eds. Springer Berlin Heidelberg, 2000, pp. 123-132.
  • S. N. Srihari, Aihua Xu, and M. K. Kalera, “Learning strategies and classification methods for off-line signature verification,” in 9th Int. Workshop on Frontiers in Handwriting Recog., Oct 2004, pp. 161–166.
  • " target="_blank">CSV

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