Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform /:
Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing, feature extraction and matching. Biometric tech...
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Hamburg, Germany :
Diplomica Verlag GmbH : Anchor Academic Publishing,
2018.
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| Zusammenfassung: | Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing, feature extraction and matching. Biometric techniques are widely used in the security world. The various types of biometric systems use different techniques for the preprocessing, feature extraction and classifiers. The dorsum of the hand is known as the finger back surface. It is highly used for personal authentication and has not yet attracted the attention of convenient researchers. It is mostly used due to contact free image acquisition. It is reported that the skin pattern on the finger-knuckle is extremely rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Furthermore, advantages of using Finger Knuckle Print (FKP) include rich in texture features, easily accessible, contact-less image acquisition, invariant to emotions and other behavioral aspects such as tiredness, stable features and acceptability in the society. As a result of that, there is less known use of finger knuckle pattern in commercial or civilian applications. The local features of an enhanced palmprint image are extracted using Fast Discrete Orthonormal Stockwell Transform (FDOST). The Fourier transform of an image is obtained by increasing the scale of FDOST to infinity. The Fourier transform coefficients extracted from the palmprint image and FKP image are considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The global feature is exploited to refine the arrangement of FKP images in matching. The proposed schemes make use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance of two FKP images. The investigational results indicate that the proposed works outperform the existing works. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9783960677031 3960677030 |
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| 245 | 1 | 0 | |a Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / |c N.B. Mahesh Kumar, Dr. K. Premalatha. |
| 264 | 1 | |a Hamburg, Germany : |b Diplomica Verlag GmbH : |b Anchor Academic Publishing, |c 2018. | |
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| 520 | |a Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing, feature extraction and matching. Biometric techniques are widely used in the security world. The various types of biometric systems use different techniques for the preprocessing, feature extraction and classifiers. The dorsum of the hand is known as the finger back surface. It is highly used for personal authentication and has not yet attracted the attention of convenient researchers. It is mostly used due to contact free image acquisition. It is reported that the skin pattern on the finger-knuckle is extremely rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Furthermore, advantages of using Finger Knuckle Print (FKP) include rich in texture features, easily accessible, contact-less image acquisition, invariant to emotions and other behavioral aspects such as tiredness, stable features and acceptability in the society. As a result of that, there is less known use of finger knuckle pattern in commercial or civilian applications. The local features of an enhanced palmprint image are extracted using Fast Discrete Orthonormal Stockwell Transform (FDOST). The Fourier transform of an image is obtained by increasing the scale of FDOST to infinity. The Fourier transform coefficients extracted from the palmprint image and FKP image are considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The global feature is exploited to refine the arrangement of FKP images in matching. The proposed schemes make use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance of two FKP images. The investigational results indicate that the proposed works outperform the existing works. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed March 29, 2019). | |
| 505 | 0 | |a Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform; TABLE OF CONTENTS; CHAPTER 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 | |
| 505 | 8 | |a 1.9.1 College of Engineering -- Pune (COEP) Palmprint Datasets1.9.2 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 | |
| 505 | 8 | |a CHAPTER 2 FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING FAST DISCRETE ORTHONORMAL STOCKWELL TRANSFORM2.1 Overview of Fast Discrete orthonormal Stockwell transform; 2.2 Local -- Global Feature Extraction and Matching; 2.2.1 Local Feature; 2.2.2 Global Feature; 2.3 Local global information fusion for knuckle-print recognition; 2.4 Experimental results and discussion; 2.5 Summary; CHAPTER 3 CONCLUSIONS AND FUTURE WORK; 3.1 SUMMARY AND CONCLUSIONS; 3.2 FUTURE WORKS; REFERENCES | |
| 650 | 0 | |a Biometric identification. |0 http://id.loc.gov/authorities/subjects/sh2001010964 | |
| 650 | 6 | |a Identification biométrique. | |
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| 650 | 7 | |a Biometric identification |2 fast | |
| 700 | 1 | |a Premalatha, K., |e author. | |
| 776 | 0 | 8 | |i Print version: |a Kumar, N.B. Mahesh. |t Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform. |d Hamburg, Germany : Diplomica Verlag GmbH : Anchor Academic Publishing, 2018 |z 3960672039 |z 9783960672036 |w (OCoLC)1017969431 |
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| contents | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform; TABLE OF CONTENTS; CHAPTER 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 Datasets1.9.2 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 CHAPTER 2 FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING FAST DISCRETE ORTHONORMAL STOCKWELL TRANSFORM2.1 Overview of Fast Discrete orthonormal Stockwell transform; 2.2 Local -- Global Feature Extraction and Matching; 2.2.1 Local Feature; 2.2.2 Global Feature; 2.3 Local global information fusion for knuckle-print recognition; 2.4 Experimental results and discussion; 2.5 Summary; CHAPTER 3 CONCLUSIONS AND FUTURE WORK; 3.1 SUMMARY AND CONCLUSIONS; 3.2 FUTURE WORKS; REFERENCES |
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| id | ZDB-4-EBA-on1090060250 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:24Z |
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| spelling | Kumar, N. B. Mahesh, author. Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / N.B. Mahesh Kumar, Dr. K. Premalatha. Hamburg, Germany : Diplomica Verlag GmbH : Anchor Academic Publishing, 2018. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references. Biometrics refers to the authentication techniques that depend on measurable physical characteristics and behavioural characteristics to identify an individual. The biometric systems consist of different stages such as image acquisition, preprocessing, feature extraction and matching. Biometric techniques are widely used in the security world. The various types of biometric systems use different techniques for the preprocessing, feature extraction and classifiers. The dorsum of the hand is known as the finger back surface. It is highly used for personal authentication and has not yet attracted the attention of convenient researchers. It is mostly used due to contact free image acquisition. It is reported that the skin pattern on the finger-knuckle is extremely rich in texture due to skin folds and creases, and hence, can be considered as a biometric identifier. Furthermore, advantages of using Finger Knuckle Print (FKP) include rich in texture features, easily accessible, contact-less image acquisition, invariant to emotions and other behavioral aspects such as tiredness, stable features and acceptability in the society. As a result of that, there is less known use of finger knuckle pattern in commercial or civilian applications. The local features of an enhanced palmprint image are extracted using Fast Discrete Orthonormal Stockwell Transform (FDOST). The Fourier transform of an image is obtained by increasing the scale of FDOST to infinity. The Fourier transform coefficients extracted from the palmprint image and FKP image are considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The global feature is exploited to refine the arrangement of FKP images in matching. The proposed schemes make use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance of two FKP images. The investigational results indicate that the proposed works outperform the existing works. Online resource; title from PDF title page (EBSCO, viewed March 29, 2019). Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform; TABLE OF CONTENTS; CHAPTER 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 Datasets1.9.2 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 CHAPTER 2 FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING FAST DISCRETE ORTHONORMAL STOCKWELL TRANSFORM2.1 Overview of Fast Discrete orthonormal Stockwell transform; 2.2 Local -- Global Feature Extraction and Matching; 2.2.1 Local Feature; 2.2.2 Global Feature; 2.3 Local global information fusion for knuckle-print recognition; 2.4 Experimental results and discussion; 2.5 Summary; CHAPTER 3 CONCLUSIONS AND FUTURE WORK; 3.1 SUMMARY AND CONCLUSIONS; 3.2 FUTURE WORKS; REFERENCES Biometric identification. http://id.loc.gov/authorities/subjects/sh2001010964 Identification biométrique. COMPUTERS General. bisacsh Biometric identification fast Premalatha, K., author. Print version: Kumar, N.B. Mahesh. Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform. Hamburg, Germany : Diplomica Verlag GmbH : Anchor Academic Publishing, 2018 3960672039 9783960672036 (OCoLC)1017969431 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2070413 Volltext |
| spellingShingle | Kumar, N. B. Mahesh Premalatha, K. Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform; TABLE OF CONTENTS; CHAPTER 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 Datasets1.9.2 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 CHAPTER 2 FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING FAST DISCRETE ORTHONORMAL STOCKWELL TRANSFORM2.1 Overview of Fast Discrete orthonormal Stockwell transform; 2.2 Local -- Global Feature Extraction and Matching; 2.2.1 Local Feature; 2.2.2 Global Feature; 2.3 Local global information fusion for knuckle-print recognition; 2.4 Experimental results and discussion; 2.5 Summary; CHAPTER 3 CONCLUSIONS AND FUTURE WORK; 3.1 SUMMARY AND CONCLUSIONS; 3.2 FUTURE WORKS; REFERENCES Biometric identification. http://id.loc.gov/authorities/subjects/sh2001010964 Identification biométrique. COMPUTERS General. bisacsh Biometric identification fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2001010964 |
| title | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / |
| title_auth | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / |
| title_exact_search | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / |
| title_full | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / N.B. Mahesh Kumar, Dr. K. Premalatha. |
| title_fullStr | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / N.B. Mahesh Kumar, Dr. K. Premalatha. |
| title_full_unstemmed | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / N.B. Mahesh Kumar, Dr. K. Premalatha. |
| title_short | Finger Knuckle-Print Authentication Using Fast Discrete Orthonormal Stockwell Transform / |
| title_sort | finger knuckle print authentication using fast discrete orthonormal stockwell transform |
| topic | Biometric identification. http://id.loc.gov/authorities/subjects/sh2001010964 Identification biométrique. COMPUTERS General. bisacsh Biometric identification fast |
| topic_facet | Biometric identification. Identification biométrique. COMPUTERS General. Biometric identification |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2070413 |
| work_keys_str_mv | AT kumarnbmahesh fingerknuckleprintauthenticationusingfastdiscreteorthonormalstockwelltransform AT premalathak fingerknuckleprintauthenticationusingfastdiscreteorthonormalstockwelltransform |