Fingerprint recognition
- 1. Fingerprint Recognition Using Minutiae-Based Features
- 2. Content •What is Fingerprint Recognition ? •What is a Fingerprint ? •Techniques for Fingerprint Recognition •Pre-processing stage •Minutiae extraction stage •Post-processing stage
- 3. Introduction Biometric: A human generated signal or attribute for authenticating a person’s identity Operate on Behavioral/Physical features Physical biometric features 1.Face 2.Fingerprint 3.Iris 4.Signature 5.voice
- 4. Introduction •Fingerprints are most useful biometric feature in our body. •Due to their durability, stability and uniqueness fingerprints are considered the best passwords. •In places of access security, high degree authentication, and restricted entry, fingerprints suggests easy and cheap solutions.
- 5. What Is Fingerprint Recognition ? •The fingerprint recognition problem can be grouped into two sub-domains: –fingerprint verification –fingerprint identification. • Fingerprints are highly reliable for of identification because of their uniqueness and consistency over time.
- 6. What Is Fingerprint ? •A fingerprint is collection of many ridges and furrows (Valleys). •The continuous dark pattern flow in fingerprint is called ridges and the light area between ridges is called furrows. •Fingerprint has some unique points on the ridge which is known as minutiae point.
- 7. Fingerprint
- 8. Why Fingerprints? The advantages of using fingerprint •fingerprint identification is one of the most reliable identification technique •Fingerprint identification is acceptable in a court of law. •A fingerprint is an individual characteristic •Fingerprints will remain unchanged during an individual‘s lifetime
- 9. Fingerprint Patterns & Features Patterns : - 3 basic patterns of fingerprint ridges are found • Arch - ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger. • Loop - ridges enter from one side of a finger, form a curve, and tend to exit from the same side they enter. • Whorl - ridges form circularly around a central point on the finger
- 10. Fingerprint Patterns & Features Minutia Features : - Minutiae are major features of a fingerprint, using which comparisons of one print with another can be made. • Ridge ending - the abrupt end of a ridge. • Ridge bifurcation - a single ridge that divides into two ridges • Short ridge, or independent ridge - a ridge that commences, travels a short distance and then ends • Island - a single small ridge inside a short ridge or ridge ending that is not connected to all other ridges • Ridge enclosure - a single ridge that bifurcates and reunites shortly afterward to continue as a single ridge • Spur - a bifurcation with a short ridge branching off a longer ridge • Crossover or bridge - a short ridge that runs between two parallel ridges • Delta - a Y-shaped ridge meeting • Core - a U-turn in the ridge pattern
- 11. Fingerprint Patterns & Features
- 12. Fingerprint Patterns & Features •Most Frequently used minutiae in applications
- 13. Techniques For Fingerprint Matching •Correlation-based matching •Minutiae-based matching - minutiae capture much of individual info. -minutiae based representations are storage efficient. - robust to fingerprint degradation.
- 14. Flowchart for Minutia Extraction
- 15. Flow of the presentation: This particular method of fingerprint matching consists mainly of six stages …. 1.Image Acquisition 2.Image Enhancement 3.Binarization 4.Segmentation 5.Thinning 6.Minutiae extraction 7.Post processing.
- 16. Image Acquisition •Automated fingerprint verification systems use live-scan digital images of fingerprints acquired from a fingerprint sensor. •Technologies used to capture image of the fingerprint •Optical •Capacitance •Ultrasonic •Thermal
- 17. Image Enhancement •The performance of minutiae extraction algorithms depends on quality of images •In general, due to skin conditions (e.g. dry, wet, bruise, etc.), sensor noise, incorrect finger pressure, and inherent low quality fingers, many fingerprints acquired are of low quality. •Leads to problems in minutiae extraction •Enhancement improves the clarity of ridge and valley structures in the fingerprint images. •Histogram equalization method is used for image enhancement
- 19. Binarization • A Fingerprint-Image-Binarization transforms an 8-bit gray image to a 1-bit binarized image where 0-value holds for ridges and 1-value for furrows. • An adaptive binarization method is achieved to binarize the fingerprint image.
- 20. Binarization
- 21. Segmentation •In a fingerprint image there are foreground regions and the background regions .The foreground regions show the ridges and valleys while the background regions are to be left out. •Segmentation separates the foreground regions from the background image for reliable extraction of minutiae using morphological methods .
- 22. Minutiae Extraction Thinning •Thinning is a morphological operation that is used to remove selected foreground pixels from binary images •Reduces the width of the ridges to one pixel •Filling holes, removing small breaks, eliminating bridges between ridges etc.
- 23. Thinning
- 24. Minutiae Marking •A simple image scan allows the pixel corresponding to minutiae to be detected •In minutiae marking the concept of Crossing Number (CN) is mainly used. •we have ridge pixels with 3 ridge pixel neighbors as ridge bifurcations and those with one ridge pixel neighbor as ridge ending. •crossing number of a pixel p =1 Ridge Ending •crossing number of a pixel p =3 Ridge Bifurcation
- 25. Minutiae Matching •Minutiae matching are performed for verification. Basically, minutiae Matching are a process which completed in two steps: 1) Find Total Minutia Points: 2) Find Location of Minutiae Points:
- 27. Overall Process Image Enhancement Binarization Thinning Sensor Matching Result Fingerprint Database Minutiae Extraction
- 28. Merits •Physical attributes are much tougher to be faked than ID cards. •Fingerprints can’t be guessed unlike passwords. •Fingerprints can’t be misplaced unlike a card. •Fingerprints can’t be forgotten unlike passwords. •Sudden enhancement in the current security level. •Less security concerns leads to increased productivity.
- 29. Demerits •High efficiency needed as the fields of application are related to security. • It can be deceived by a picture or a mold of finger using Gelatin. •Fingerprints if stolen, can be a great threat to Security and intellectual property. •Requires a very large data base of fingerprints. •Some of the employees may find it uncomfortable to Have their fingerprint stored with the employer.
- 30. Applications •Financial services (e.g. ATM ) •Immigration & border control (e.g. points of entry declared for frequent travelers, passport and visa cases ) •Social services (e.g. fraud preventation in entitlement programmers) •Health care (e.g. security measure for privacy or medical records) •Physical access control (e.g. at institutional, government & residential establishment) •Time & attendance (e.g. replacement of time punch card) •Computer Security (e.g. personal computer access, network access, Internet use, e-commerce, e-mail, encryption) •Telecommunications (e.g. mobile phones, call center technology, phone cards, televised shopping) •Law enforcement (e.g. criminal investigation, national ID, driving license, rehabilitation institutions/prison, home confinement, small gun)
- 31. Future Work We can improve the results by – Enhancing the image by completing ridges. Considering average ridge thickness. A more robust algorithm for minutiae matching.
- 32. References •Raymond Thai, ‘Fingerprint Image Enhancement and Minutiae- Extraction’,Thesis submitted to School of Computer Science and Software Engineering, University of Western Australia •AK Jain, A. Ross, and S. Prabhakar, Fingerprint Matching Using Minutiae and Texture Features , Proc. of International Conference on Image Processing, 2001 •Digital Image Processing by Rafael C. Gonzalez and Richard E. Woods, Pearson Education, 2003 •Digital Image Processing using MATLAB: Rafael C. Gonzalez, Richard E. Woods 2nd Edition, 2009 •Online Fingerprint Verification by Sharat Chikkerur CUBS, University of Buffalo
- 33. Thank You