This book presents a novel method of multimodal biometric fusion using a random selection of biometrics, which covers a new method of feature extraction, a new framework of sensor-level and feature-level fusion. Most of the biometric systems presently use unimodal systems, which have several limitations. Multimodal systems can increase the matching accuracy of a recognition system. This monograph shows how the problems of unimodal systems can be dealt with efficiently, and focuses on multimodal biometric identification and sensor-level, feature-level fusion. It discusses fusion in biometric systems to improve performance.
* Presents a random selection of biometrics to ensure that the system is interacting with a live user.
* Offers a compilation of all techniques used for unimodal as well as multimodal biometric identification systems, elaborated with required justification and interpretation with case studies, suitable figures, tables, graphs, and so on.
* Shows that for feature-level fusion using contourlet transform features with LDA for dimension reduction attains more accuracy compared to that of block variance features.
* Includes contribution in feature extraction and pattern recognition for an increase in the accuracy of the system.
* Explains contourlet transform as the best modality-specific feature extraction algorithms for fingerprint, face, and palmprint.
This book is for researchers, scholars, and students of Computer Science, Information Technology, Electronics and Electrical Engineering, Mechanical Engineering, and people working on biometric applications.
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Academic and Professional Reference
Illustrationen
84 s/w Zeichnungen, 20 s/w Tabellen, 108 s/w Abbildungen, 24 s/w Photographien bzw. Rasterbilder
20 Tables, black and white; 84 Line drawings, black and white; 24 Halftones, black and white; 108 Illustrations, black and white
Maße
Höhe: 240 mm
Breite: 161 mm
Dicke: 12 mm
Gewicht
ISBN-13
978-1-032-66058-5 (9781032660585)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Sampada Dhole has completed his PhD in Electronics from Bharati Vidyapeeth (Deemed to be University) College of Engineering, India, in 2017 with specialisation in Image Processing and Biometrics. Her research interest includes the Image Processing and Multimodal. She has published more than 30 research papers including 7 Scopus indexed. She has filed 2 patents and 1 copyright in her technical field. She has worked as a Reviewer for many International and National Conferences. She is working as Assistant Professor in the Department of E&TC at Bharati Vidyapeeth's College of Engineering for Women, SPPU, Pune, India. She has 21 years of teaching experience. She is a member of the Technical Society ISTE, India.
Vinayak Bairagi has completed ME (Electronics) from Sinhgad COE, Pune, India, in 2007 (1st Rank in SPPU). Savitribai Phule Pune University has awarded him a PhD degree in Engineering. He has teaching experience of 13 years and research experience of 8 years. He has filed 12 patents and 5 copyrights in his technical field. He has published more than 60 papers, of which 26 papers are in international journals. He has authored/edited more than eight books/book chapters with multiple publishing concerns and he is a reviewer for nine scientific journals. He has received grants from DST SERB, UoP-BUCD, GYTI. He has received more than 14 awards, which include the National Level Young Engineer Award (2014), the ISTE National level Young Researcher Award (2015) for his excellence in the field of engineering, and IETE M N SAHA Memorial Award-2018. He is a member of INENG (UK), IETE (India), ISTE (India), and IEI & BMS (India). He had worked on Image Compression at the College of Engineering, Pune, under Pune University. His main research interests include Medical Imaging, Machine Learning, Computer-Aided Diagnosis, and Medical Signal Processing. Currently, he is associated with the AISSMS Institute of Information Technology, Pune, India, as Professor in Electronics and Telecommunication Engineering. He is a recognised PhD guide in Electronics Engineering of Savitribai Phule Pune University. Presently he is guiding seven PhD students.
Preface.................................................................................................................... viii
Author Biography........................................................................................................x
Chapter 1 Introduction...........................................................................................1
1.1 Biometric Identification System.................................................1
1.1.1 Enrolment Module........................................................2
1.2 Current Status of Biometric Identification Systems...................3
1.3 Applications of Biometric Systems............................................5
References.............................................................................................5
Chapter 2 An Overview of Biometrics..................................................................6
2.1 Biometrics...................................................................................6
2.1.1 Advantages of Biometrics.............................................7
2.1.2 Disadvantages of Biometrics.........................................8
2.1.3 Types of Biometrics.......................................................8
2.2 Fingerprint..................................................................................8
2.2.1 Minutiae-based Technique............................................9
2.2.2 Correlation-based Technique........................................9
2.2.3 Advantages and Disadvantages of Fingerprint
Biometrics.....................................................................9
2.2.4 Applications of Fingerprinting.................................... 10
2.3 Iris Recognition........................................................................ 10
2.3.1 Advantages of Iris Technology.................................... 10
2.3.2 Disadvantages of Iris Technology............................... 10
2.3.3 Applications of Iris Recognition System..................... 11
2.3.4 Real-Life Applications................................................ 11
2.4 Retinal Pattern Biometrics....................................................... 11
2.4.1 Advantages of Retinal Recognition............................. 12
2.4.2 Disadvantages of Retinal Recognition........................ 12
2.5 Facial Recognition Biometrics................................................. 12
2.5.1 Challenges in Face Recognition.................................. 13
2.5.2 Advantages of Biometric Facial Recognition.............. 13
2.5.3 Disadvantages of Biometric Face Recognition........... 13
2.5.4 Applications................................................................. 13
2.6 Handwriting.............................................................................. 14
2.6.1 Advantages and Disadvantages of Handwriting
Recognition................................................................. 14
2.7 Voice Biometric........................................................................ 14
2.7.1 Advantages.................................................................. 15
2.7.2 Disadvantages.............................................................. 15
2.8 Ear Recognition........................................................................ 15
2.8.1 Advantages.................................................................. 15
2.8.2 Disadvantages.............................................................. 15
2.9 Summary.................................................................................. 16
Chapter 3 Motivation behind Multimodal Biometric Systems............................ 17
3.1 Introduction.............................................................................. 17
3.1.1 Advantages of Multimodal Systems over
Unimodal Systems...................................................... 18
3.2 Multimodal Biometric Integration Architecture...................... 19
3.3 Multimodal Biometric Integration Scenarios........................... 19
3.4 Multimodal Biometric Fusion Levels....................................... 21
3.4.1 Pre-mapping Fusion.................................................... 21
3.4.2 Post-mapping Fusion...................................................25
References...........................................................................................28
Chapter 4 Performance Measurement Parameters for Biometric Systems.......... 31
4.1 Performance Measurement Parameters.................................... 31
4.2 Materials................................................................................... 33
4.2.1 Fingerprint Database...................................................34
4.2.2 Face Database..............................................................34
4.2.3 Hand Database............................................................ 35
4.3 Summary.................................................................................. 35
Reference............................................................................................. 35
Chapter 5 Unimodal Biometric Systems..............................................................36
5.1 Unimodal Biometric Identification System..............................36
5.1.1 DWT Feature Extraction System................................ 37
5.1.2 Gabor Feature Extraction System...............................38
5.1.3 Curvelet Transform.....................................................40
5.1.4 Contourlet Transform.................................................. 41
5.2 Fingerprint as a Biometric Modality........................................ 41
5.2.1 Techniques for Fingerprint Matching......................... 42
5.2.2 Minutiae-Based Feature Extraction System................ 42
5.2.3 Texture-Based Fingerprint Recognition System......... 45
5.3 Face as a Biometric Modality...................................................49
5.3.1 Texture-Based Face Recognition System....................49
5.4 Hand Geometry as a Biometric Modality................................ 51
5.4.1 Hand Geometry Recognition Using 12 Geometry
Features....................................................................... 55
5.4.2 Hand Geometry Recognition Using 21 Geometry
Features.......................................................................56
5.5 Palmprint as a Biometric Modality.......................................... 58
5.5.1 Contourlet Transform..................................................63
Contents vii
5.6 Euclidean Distance as a Classifier............................................ 67
5.7 Summary.................................................................................. 71
References........................................................................................... 71
Chapter 6 Multimodal Biometric Identification Systems Using
Sensor-Level Fusion............................................................................ 72
6.1 Multimodal Biometric Identification System........................... 72
6.2 Sensor-Level Fusion................................................................. 72
6.3 Basic Structure for Sensor-Level Fusion.................................. 73
6.4 Sensor-Level Fusion of Low-Frequency and High-
Frequency Features................................................................... 75
6.5 Sensor-Level Fusion of Low-Frequency Features.................... 78
6.6 Summary.................................................................................. 81
Chapter 7 Multimodal Biometric Identification Systems Using
Feature-Level Fusion...........................................................................82
7.1 Multimodal Biometric System.................................................82
7.2 Feature-Level Fusion Using Block Variance Features.............83
7.2.1 Feature-Level Fusion of 128 Feature Vector...............83
7.2.2 Feature-Level Fusion of 32 Feature Vector.................85
7.2.3 Concatenated Features................................................ 91
7.2.4 Sum Features...............................................................92
7.2.5 Maximum Features.....................................................92
7.2.6 Minimum Features......................................................92
7.3 Feature-Level Fusion Using Contourlet Transform Features...92
7.4 Normalisation Technique for Hand Geometry Features..........95
7.5 Linear Discriminate Analysis (LDA).......................................97
7.6 Summary................................................................................ 100
Chapter 8 Result and Discussion....................................................................... 101
8.1 Result and Discussion............................................................. 101
8.1.1 Databases Used......................................................... 101
8.1.2 Results of Performance Measurement
Parameters of the Biometric Systems....................... 101
8.1.3 Results of Performance Measurement
Parameters of Multimodal Recognition System....... 104
8.1.4 Score Distribution of Biometric System.................... 113
8.1.5 Analysis..................................................................... 120
8.2 Conclusions............................................................................. 122
8.3 Future Scope...........................................................................124
Index....................................................................................................................... 125
