1 / 18

Fingerprint Verification

Explore the advantages of using fingerprint identification, one of the most reliable biometric techniques. Learn about minutia-based and image-based approaches in automated fingerprint identification systems and how signatures are used for classification. Understand the process of minutiae extraction, matching, and orientation estimation in an online fingerprint verification system using MATLAB implementation.

kmoses
Download Presentation

Fingerprint Verification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fingerprint Verification Bhushan D Patil PhD Research Scholar Department of Electrical Engineering Indian Institute of Technology, Bombay Powai, Mumbai 400076

  2. Introduction • Biometric: A human generated signal or attribute for authenticating a person’s identity • Different biometric features 1.Face 2.Fingerprint 3.Iris 4.Signature 5.voice

  3. Why Fingerprint • The advantages of using fingerprint • fingerprint identification is one of the most reliable identification technique • Its validity is justified • It is most commonly used biometrics technique • Basic Approaches Minutia Based Approach Image Based Approach

  4. Automated Fingerprint Identification System

  5. Fingerprint Classification

  6. Signatures • Use signatures to determine if two fingerprints are from same finger • Ridge Endings • Ridge Bifurcations • These are termed “minutia”

  7. Minutiae

  8. Desired Information Correspondences between template and input F.P. are known There are no deformations (translations, rotation, non-linear deformations) Each minutia is exactly localized Minutiae Point Pattern Matching Real Situation • No correspondence is known beforehand • There are deformations • Spurious minutiae are present in templates and input images • Some minutiae are missed

  9. On-Line F.P. Verification System

  10. Minutia Extraction Estimation of Orientation Field Identify fingerprint region Ridge extraction Cleaning ridge segments Minutia extraction

  11. Estimation of Orientation Field • Orientation is the angle formed by the ridges with the horizontal axis • Find the local orientation of the ridge in small areas of the image • Steps Divide image into blocks of size WxW Compute gradients [Gx Gy]at each pixel in block Orientation at each block

  12. Minutia Extraction • Ridge Ending • Ridge Bifurcation

  13. Minutia Matching Point Pattern Alignment Matching Scoring

  14. MATLAB Implementation • GUI demo……….

  15. Thank You

More Related