PPT-Minutiae Local Structures for Fingerprint Matching and Indexing

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 --Presented By . Sudheer. . Chelluboina. .. Professor: . Dr.Maggie. Dunham. Contents . Outline of Paper. Introduction . Index Structures. Due to rapid increase in the use of location based services applications, large amount of location data of moving object is recorded. Because of that efficient indexing techniques are required to manage these large amounts of trajectory data. All index structures are focused on either indexing past, current and future locations. Every indexing structure or techniques discussed in this paper will make simpler indexing or it will increase the overall query processing performance. . 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. Akhil. . Vij. Anoop. . Namboodiri. . Overview. 2. Introduction. Major Challenges . Motivation. Local Structures for Indexing. Local Structures for Matching. Summary and Conclusion. Introduction. 3. 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. 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. 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).