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Signature verification using grid based feature extraction.

Signature verification using grid based feature extraction.

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Sign verification Sign verification Presentation Transcript

  • Signature Verification usingGrid based feature extraction Aniket Sahasrabuddhe Anurag D. Y. Patil College Of Engineering Shashank Akurdi, Pune Sushant Saurav
  • Presentation Agenda  Introduction  Existingtechniques  Proposed work  Algorithms  Mathematical model  Advantages & disadvantages  Conclusion
  • Introduction  Computers are largely used in almost each and every field.  Thesecurity measures to be used must be cheap, reliable and un-intrusive to the authorized person. View slide
  • Importance of Signature  Transaction  Individuals less likely to object  Biometric View slide
  • Types of signature Identification  Offline Signature Verification  deals with shape only  Online signature Verification  deals with dynamic features like speed, pen pressure, directions, stroke length, and when the pen is lifted from the paper
  • Existing systems and limitations UsingVariable Length Segmentation and Hidden Markov Models New extreme points warping technique Wavelet Transform Based Global Features • Percentage of error occurrence is high • It has heavy computational load • Optimal performance not guaranteed
  • Challenging tasks  Differentiating between the parts of the signature that vary with each signing.  The signature can vary substantially over an individual’s lifetime.
  • Proposed Work  Signature Acquisition  Signature Pre-processing  Feature Extraction  Signature Verification Fig 1 System Architecture
  • Signature Acquisition  Signature is acquired from the user. Fig.2 Sample Signatures
  • Signature Pre-processing Function of the preprocessor Image resizing Image binarizing Image thinning Image normalizing
  • Feature Extraction Fig.3 Grid over pre-processed signature image Fig.4 Matrix corresponding to the above grid
  • Signature Verification Fig.5 Signature Verification
  • Applications  Banking,  Passport office,  And any other places which require identification !
  • Advantages  Low error rate.  Forgery is detected even when the forger has managed to get a copy of the authentic signature.  Fast and simple training.  Cheap hardware.  Little storage requirements.
  • Use Case Diagram
  • Class Diagram
  • Activity Diagram
  • Component Diagram
  • Sequence Diagram
  • Collaboration Diagram
  • State Diagram
  • Requirements & Technologies The hardware component we are using here is a scanning device(WEBCAM), high RAM for better results and good processor. • Operating System: Windows XP or Higher • NetBeans IDE 7.0 • SDK - J2SE • Intel core 2 duo processor • 2.1 GHZ, 1 GB RAM
  • Conclusion  The pre-processed signature i.e. resized, binarized, thinned and rotation normalized signature is segmented into grid of size 10x20 cells where each cell is having 100 pixels.  The system does not need any special hardware like tablet, fingerprint verification or iris scanning systems.  It requires only low cost webcams  The database used for the verification will not be large.
  • References  Muhammed Nauman Sajid “Vital Sign: Personal Signature based Biometric Authentication System”, Bs degree thesis, Pakistan Institute of Engineering and Applied sciences, sept 2009.  K. Yasuda, D. Muramatsu, and T. Matsumoto, “Visual-based online signature verification by pen tip tracking”, Proc. CIMCA 2008, 2008, pp. 175–180.  D.Muramatsu, M. Kondo, M. Sasaki, S. Tachibana, and T. Matsumoto. “A markov chain monte carlo algorithm for bayesian dynamic signature verification”. IEEE Transactionson Information Forensics and Security, 1(1):22–34, March,2006.  Satoshi Shirato, D. Muramatsu, and T. Matsumoto, “camera-based online signature verification: Effects of camera positions.” World Automation congress2010 TSI press.  D. Muramatsu, K. Yasuda, S. Shirato, and T. Matsumoto. “Visual-based online signature verification using features extracted from video”, Journal of Network and Computer Applications Volume 33, Issue 3, May 2010, Pages 333-341.
  • Cont.  M. E. Munich and P. Perona. “Visual identification by signature tracking.” IEEE Trans. Pattern Analysis and MachineIntelligence, 25(2):200–217, February 2003.  F.A.Afsar, M. Arif and U. Farrukh, “Wavelet Transform Based Global Features for Online Signature Recognition”, Proceeding of IEEE International Multi- topic Conference INMIC, pp. 1-6 Dec. 2005.  Charles E. Pippin, “Dynamic Signature Verification using Local and Global Features”, Georgia Institute of Technology, July 2004.  Hao Feng and Chan Choong Wah, “Online Signature Verification Using New Extreme Points Warping Technique”, Pattern Recognition Letters, vol. 24, pp. 2943-2951, Dec. 2003.  F.A. Afsar, M. Arif and U. Farrukh, “Wavelet Transform Based Global Features for Online Signature Recognition”, Proceeding of IEEE International Multi- topic Conference INMIC, pp. 1-6 Dec. 2005.
  • Thank You