Biometric methods for authenticating and identifying people are increasingly used in both the commercial and private sector. Today’s commercially available biometric systems show good reliability. However, they generally lack user acceptance. Users showed an antipathy towards touching a possibly dirty fingerprint scanner, or looking into an iris scanner that might malfunction and eventually impair their vision. Whether those fears are well founded or not is less important. The fact is, they have considerable influence on user acceptance. And user consent is important for a good and successful application of a biometric system, as well as for good recognition rates.
In response to the increasing demand for reliable as well as user friendly biometric systems, this work investigates the applicability of palmprint and FKP were the biometric features for authentication. Using palmprint or FKP as a biometric system avoids such problems as shown before, since it requires no subject interaction.
The main objectives of the thesis are: to propose the transform based techniques that is used to achieve higher recognition accuracy and lower equal error rate; and to examine the performance of the proposed techniques with the existing methodologies.
TABLE OF CONTENTS
1 INTRODUCTION TO BIOMETRICS
1.1 INTRODUCTION
1.1.1 Biometric Systems
1.2 PALMPRINT BIOMETRICS
1.2.1 Preprocessing and ROI Extraction for Palmprint Biometrics
1.3 FINGER KNUCKLE- PRINT BIOMETRICS
1.3.1 Finger Knuckle-Print Anatomy
1.3.2 Preprocessing and ROI Extraction for Finger Knuckle-Print Biometrics
1.4 PROS OF FINGER KNUCKLE-PRINT AND PALMPRINT
1.5 LOCAL AND GLOBAL FEATURES
1.6 PROBLEM STATEMENT
1.7 MOTIVATION
1.8 OBJECTIVES
1.9 BIOMETRIC DATASETS
1.9.1 College of Engineering – Pune (COEP) Palmprint Datasets
1.9.2 The PolyU Palmprint Datasets
1.9.3 Indian Institute of Technology (IIT Delhi) Touchless Palmprint Datasets
1.9.4 The PolyU Finger Knuckle-print Datasets
1.10 PERFORMANCE METRICS
1.10.1 False Acceptance Rate and False Rejection Rate
1.10.2 Speed
1.10.3 Equal Error Rate (EER)
1.10.4 Correct Classification Rate (CCR)
1.10.5 Data Presentation Curves
1.10.5.1 Receiver Operating Characteristic (ROC) Curve
2 LOCAL AND GLOBAL FEATURE EXTRACTION USING WINDOW WIDTH OPTIMIZED STOCKWELL TRANSFORM IN PALMPRINT BIOMETRIC SYSTEM
2.1 OVERVIEW OF WINDOW WIDTH OPTIMIZED S-TRANSFORM
2.1.1 Algorithm for Determining the Time Invariant p
2.1.2 Algorithm for Determining p(t)
2.1.3 Inverse of the WWOST
2.2 LOCAL - GLOBAL FEATURE EXTRACTION AND MATCHING CHAPTER NO. TITLE PAGE NO
2.2.1 Local Feature
2.2.2 Global Feature
2.2.2.1 Phase-only correlation
2.2.2.2 Band-limited phase-only correlation
2.3 LOCAL GLOBAL FEATURE FUSION FOR PALMPRINT RECOGNITION
2.4 EXPERIMENTAL RESULTS AND DISCUSSION
2.5 SUMMARY
3 CONCLUSIONS
3.1 SUMMARY AND CONCLUSIONS
REFERENCES
LIST OF FIGURES
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