PPT-Minutiae Local Structures for Fingerprint Matching and Inde

Akhil Vij Anoop Namboodiri Overview 2 Introduction Major Challenges Motivation Local Structures for Indexing Local Structures for Matching Summary and Conclusion Introduction 3. Minutiae extractionAUTHENTICATION database ENROLLMENTMinutiae extraction Minutia matching Known printUnknown print Figure1.Identicationprocedure Figure2.Ridgeendingandbifurcation-Positionandorientati Alex . Kot. School of Electrical & Electronic Engineering. Nanyang. Technological University. Singapore. Biometrics in daily life . Biometrics. Images are downloaded from the internet. Biometrics in daily life . Biometrics Once compromised, the grayscale image is the most at risk. Leakage of a phase image or skeleton image is also dangerous since it is a trivial problem to reconstruct a grayscale fingerpri Term Projects. CSE 666, . Fall 2014. Guidelines. The described projects are suggestions; if you have desire, skills or idea to explore alternative topics, you are free to do so.. . Finalize the project selection by October 16; have a 1-2 slide (2-3 minutes) presentation describing the project on that day.. AKSHAY KANCHAR(25). DHRUV PANCHAL(44). RICHA SAXENA(57). PROJECT GUIDE :- K.T.TALELE. BHARTIYA VIDYA BHAVAN'S. SARDAR PATEL INSTITUTE OF TECHNOLOGY. MUNSHI NAGAR, ANDHERI (W),. MUMBAI - 400 058.. 2011-12. years of focused world-class experience. World’s largest install base. Most industries & toughest environments. Consistently Ranked #1 by . NIST. Most refined, tested . and trusted algorithms. FBI . “Fingerprints cannot lie, . but liars can make fingerprints.”. —. Unknown. 2. Fingerprinting Merit Badge. Requirement #1. Give . a short history of fingerprinting. . Tell . the difference between civil and criminal identification. . Dakota Boyd, Dustin Short, Elizabeth Lee, John . Huppenthal. , Shelby . Proft. , . Wacey. Teller. History of Fingerprinting. Originally used paper and ink fingerprints. Fingerprints were matched using trained individuals. Face . Recognition. He Wang. , . Xuan. . Bao. , . Romit. Roy . Choudhury. , . Srihari. . Nelakuditi. Motivation – Application Scenarios. 2. 2. s. hare a ride to airport. Bob. Elle. Bret. Overview. IAFIS classification. Automated fingerprint identification system. IAFIS classification. IAFIS (. I. ntegrated . A. utomated . F. ingerprint . I. dentification . S. ystem) designations, are based on fingerprint patterns. Akhil. . Vij. Anoop. . Namboodiri. . Overview. 2. Introduction. Major Challenges . Motivation. Local Structures for Indexing. Local Structures for Matching. Summary and Conclusion. Introduction. 3. Finge sing Ridges and Valleys Paramvir Singh * Department of Computer Engineering Punjabi University Patiala, India Dr. Lakhwinder Kaur Department of Computer Engineering Punjabi University Patiala, M anuscript received Feb. 2, 1996; revised Oct. 21, 1996. Recommended for accep-tance by B. Dom.For information on obtaining reprints of this article, please send e-mail to:transpami@computer.org, and Palm Prints. Features of Palm or Palm print. Palm contains . Three flexion creases (principal lines : heart line, head line, life line) . Secondary creases (wrinkles). Ridges . Regions : Finger root (I), inside region (II) and outside region (III).