Mulitmodal Biometric Systems Munir Bandukwala Rajesh Subramanian.

Slides:



Advertisements
Similar presentations
RSS Avon Local Group, 14 October 2008 A Matching Algorithm for Paired Living Kidney Donation in the UK Joanne Allen – Senior Statistician NHS Blood and.
Advertisements

It’s a matter of perspective Mary Theofanos. 3 System starts capture Capture Capture thresholding Time Participant presents Biometric Participant withdraws.
Biometrics and Usability A Taxonomy of Definitions for Usability Studies in Biometrics Brian Stanton.
Slides from: Doug Gray, David Poole
Cognitive Modelling – An exemplar-based context model Benjamin Moloney Student No:
ETHEM ALPAYDIN © The MIT Press, Lecture Slides for.
Chapter 10 Decision Making © 2013 by Nelson Education.
1 Performance Evaluation of Score Level Fusion in Multimodal Biometric Systems Web Computing Laboratory Computer Science and Information Engineering Department.
Access Control Methodologies
Fingerprint Minutiae Matching Algorithm using Distance Histogram of Neighborhood Presented By: Neeraj Sharma M.S. student, Dongseo University, Pusan South.
Contactless and Pose Invariant Biometric Identification Using Hand Surface Vivek Kanhangad, Ajay Kumar, Senior Member, IEEE, and David Zhang, Fellow, IEEE.
Biometrics.
Designing a Multi-Biometric System to Fuse Classification Output of Several Pace University Biometric Systems Leigh Anne Clevenger, Laura Davis, Paola.
Authors: Anil K. Jain, Arun Ross and Sharath Pankanti Presented By: Payas Gupta.
Luvuyo Morris Supervisor: Mr. R. Dodds Co-Supervisor: Mr. M. Ghazi-Asgar Mentor: Mr. Mentor: Mr. Roland Foster.
Presentation of Master’s thesis Gait analysis: Is it possible to learn to walk like someone else? Øyvind Stang.
® Norman Poh Hoon Thian What is Biometric Authentication? A process of verifying an identity claim using a person’s behavioral and physiological characteristics.
Information Fusion in Multibiometric Systems
Tracking Objects with Dynamics Computer Vision CS 543 / ECE 549 University of Illinois Derek Hoiem 04/21/15 some slides from Amin Sadeghi, Lana Lazebnik,
Instructor: Dr. G. Bebis Reza Amayeh Fall 2005
Biometrics & Security Tutorial (a) Why multi-biometrics? (P13: 3-6)
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
Indexing and Binning Large Databases
Biometrics II CUBS, University at Buffalo
Biometric Authentication: Security Issues M. Fahim Zibran February 23, 2009.
Chapter 11 Integration Information Instructor: Prof. G. Bebis Represented by Reza Fall 2005.
Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics IEEE Trans on PAMI, VOL. 25, NO.9, 2003 Kyong Chang, Kevin W. Bowyer,
ICASSP'06 1 S. Y. Kung 1 and M. W. Mak 2 1 Dept. of Electrical Engineering, Princeton University 2 Dept. of Electronic and Information Engineering, The.
Deviation = The sum of the variables on each side of the mean will add up to 0 X
1J. M. Kizza - Ethical And Social Issues Module 16: Biometrics Introduction and Definitions Introduction and Definitions The Biometrics Authentication.
Module 14: Biometrics Introduction and Definitions The Biometrics Authentication Process Biometric System Components The Future of Biometrics J. M. Kizza.
Biometrics: Ear Recognition
A Framework for Detection of Anomalous and Suspicious Behavior from Agent’s Spatio-Temporal Traces Boštjan Kaluža Depratment of Intelligent Systems, Jožef.
Fusion by Biometrics 主講人:李佳明、陳明暘 指導教授:林維暘. Outline Introduction Introduction Biometric system Biometric system Feature extraction Feature extraction The.
An Introduction to Biometric Recognition Anil K
1 / 41 Inference and Computation with Population Codes 13 November 2012 Inference and Computation with Population Codes Alexandre Pouget, Peter Dayan,
Further Topics in Regression Analysis Objectives: By the end of this section, I will be able to… 1) Explain prediction error, calculate SSE, and.
Video Based Palmprint Recognition Chhaya Methani and Anoop M. Namboodiri Center for Visual Information Technology International Institute of Information.
Biometric Measures for Human Identification
Signature with Text-Dependent and Text-Independent Speech for Robust Identity Verification B. Ly-Van*, R. Blouet**, S. Renouard** S. Garcia-Salicetti*,
Today Ensemble Methods. Recap of the course. Classifier Fusion
Exploiting Context Analysis for Combining Multiple Entity Resolution Systems -Ramu Bandaru Zhaoqi Chen Dmitri V.kalashnikov Sharad Mehrotra.
1 Biometric Databases. 2 Overview Problems associated with Biometric databases Some practical solutions Some existing DBMS.
1 ISO/IEC JTC1/SC37 Standards A presentation of the family of biometric standards October 2008.
Non-Bayes classifiers. Linear discriminants, neural networks.
Designing multiple biometric systems: Measure of ensemble effectiveness Allen Tang NTUIM.
Predictive models for multibiometric systems Suresh Kumar Ramachandran Nair, Bir Bhanu, Subir Ghosh, Ninad S. Thakoor Adviser:Frank. Yeong-Sung Lin Present.
BIOMETRICS THE MAN MACHINE INTERFACE
1 Biometric template selection and update: a case study in fingerprints Source:Pattern Recognition, Vol. 37, 2004, pp Authors: Umut Uludag, Arun.
Random Forests Ujjwol Subedi. Introduction What is Random Tree? ◦ Is a tree constructed randomly from a set of possible trees having K random features.
MULTI-BIOMETRIC SYSTEM Chris Chiffriller, Chris George, Gabriel Dos Santos, Subah Sachdeva.
Non-Homogeneous Second Order Differential Equation.
1 Framework Programme 7 Evaluation Criteria. 2 Proposal Eligibility Evaluation by Experts Commission ranking Ethical Review (if needed) Commission rejection.
Medical Card System with Fingerprint Authentication Luvuyo Morris Supervisor: Mr. R. Dodds Co-Supervisor: Mr. M. Ghazi-Asgar Mentor: Mr. Roland Foster.
Introduction to Biometrics Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #6 Guest Lecture + Some Topics in Biometrics September 12,
AdaBoost Algorithm and its Application on Object Detection Fayin Li.
An Introduction to Biometrics
Multi-Modal Combination using the Biometric Gain Concept Nigel Sedgwick Cambridge Algorithmica Limited 9 Oakdene Beaconsfield Buckinghamshire United Kingdom.
INTRODUCTION TO MULTIPLE REGRESSION MULTIPLE REGRESSION MODEL 11.2 MULTIPLE COEFFICIENT OF DETERMINATION 11.3 MODEL ASSUMPTIONS 11.4 TEST OF SIGNIFICANCE.
Multimodal Biometric Security 1.
Basic derivation rules We will generally have to confront not only the functions presented above, but also combinations of these : multiples, sums, products,
BLIND AUTHENTICATION: A SECURE CRYPTO-BIOMETRIC VERIFICATION PROTOCOL
Multimodal Biometric Security
Solve more difficult number problems mentally
Match Score Fusion of Fingerprint and Face Biometrics for Verification
Lecture 9: Entity Resolution
Privacy-Preserving Multibiometric Authentication in Cloud
Review Questions III Compare and contrast the components of an individual score for a between-subject design (Completely Randomized Design) and a Randomized-Block.
Examples of biometric traits.
A Neural Passage Model for Ad-hoc Document Retrieval
Presentation transcript:

Mulitmodal Biometric Systems Munir Bandukwala Rajesh Subramanian

Introduction Biometric systems using single biometrics have to contend with Noisy data. Restricted degrees of freedom. Non universality of data. Unacceptable error rates.

Intoduction-contd… Multibiometric systems reduce these problems by providing multiple evidences of the same entity. These systems improve overall performance. Multimodal systems also make spoofing difficult. By prompting for a random set of biometric traits, such systems ensure a built in ‘liveness test’.

Problems involved A multimodal system needs a particular ‘fusion scheme’ to integrate the data from multiple modalities. Several fusion schemes are available in the literature Sum Rule [1] Decision trees [1] Linear Discriminant Analysis [1]

Problems involved-contd… Further schemes like: Learning user specific matching thresholds Weighting of individual biometric traits or A combination of the above also seem to bolster performance [2]

Problem Statement We plan to propose a fusion system that will provide an integration scheme for Fingerprint, Hand Geometry and Signature. We propose to find a scheme providing better results. The fusion will be at the matching score level.

References [1] Information Fusion in Biometrics-Arun Ross and Anil Jain [2]Learning User-specific parameters in a multibiometric system-Anil K. Jain and Arun Ross