Fingerprint Recognition System

With the advent of electronic banking, e-commerce, and smart cards and an increased emphasis on the privacy and security of information stored in various databases, automatic personal identification has become a very important topic. Accurate automatic personal identification system is now needed in a wide range of civilian applications involving the use of passports, cellular telephones, automatic teller machines, and driver licenses. Traditional knowledge-based [password or personal identification number (PIN)] and token-based (passport, driver license, and ID card) identifications are prone to fraud because PIN’s may be forgotten or guessed by an imposter and the tokens may be lost or stolen.

Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter.

A biometric system can be operated in two modes:
1) Verification mode &

2) Identification mode.

A biometric system operating in the verification mode either accepts or rejects a user’s claimed identity while a biometric system operating in the identification mode establishes the identity of the user without claimed identity information. Among all the biometrics (e.g., face, fingerprints, hand geometry, iris, retina, signature, voice print, facial thermogram, hand vein, gait, ear, odor, keystroke dynamics, etc.) fingerprint-based identification is one of the most mature and proven technique.

A fingerprint is the pattern of ridges and valleys on the surface of the finger. The uniqueness of a fingerprint can be determined by the overall pattern of ridges and valleys as well as the local ridge anomalies a ridge bifurcation or a ridge ending, called minutiae points. The critical factor in the widespread use of fingerprints is in its satisfying performance (e.g., matching speed and accuracy) requirements of the emerging civilian identification applications. Some of these applications (e.g., fingerprint- based smart cards) will also benefit from a compact representation of a fingerprint.

The popular fingerprint representation schemes have evolved from an intuitive system design tailored for fingerprint experts who visually match the fingerprints. These schemes are either based on predominantly local landmarks (e.g., minutiae-based fingerprint matching systems) or exclusively global information. The minutiae-based automatic identification techniques first locate the minutiae points and then match their relative placement in a given finger and the stored template.

The fingerprint image may be obtained from a thumb pad fingerprint scanner device scanning at 500 dpi. A good quality fingerprint contains between 60 and 80 minutiae, but different fingerprints have different number of minutiae. The minutiae extracted from the fingerprint image is stored in the database when working on enrollment mode (identification) & in the verification mode the extracted minutiae is compared with those already stored in the database .If a matching above a threshold level occurs then the person is recognized by the system.

Prominent applications of fingerprint recognition system include criminal identification, security systems in organizations, license generation, home security, automated attendance systems in colleges and universities.

References :

1. Filterbank-Based Fingerprint Matching

Anil K. Jain, Fellow, IEEE, Salil Prabhakar, Lin Hong, and Sharath Pankanti

2. Fingerprint Image Enhancement: Algorithm and Performance evaluation

Lin Hong, Student Member, IEEE, Yifei Wan, and Anil Jain, Fellow, IEEE

3. Fingerprint Enhancement in the Singular Point Area

Sen Wang, Student Member, IEEE, and Yangsheng Wang]

4. Minutiae Detection Algorithm for Fingerprint Recognition

Virginia Epinoso – Duro, Polytechnic University of Catalonia.

5. Feature Extraction—A Pattern for Information Retrieval.

Dragos¸-Anton Manolescu

6. A hybrid fingerprint matcher .Arun Rossa, Anil Jain, James Reisman

7. Digital Image Processing: Concepts, Algorithms & Scientific Applications

By Bernd Jahne

8. Image Processing In C By Dwayne Philips

9. Digital Image Processing Techniques By Anil Kumar Jain

10. Feature Extraction Using a Chaincoded Contour ,Representation of

Fingerprint Images

Venu Govindaraju, Zhixin Shi

CEDAR, Department of Computer Science and Engineering,

John Schneider

11. Software Engineering – A practitioners approach – Roger Pressman

12. IEEE Online journals

13. www.google.com