LEADER 00000cam a2200577Ii 4500 
001    on1090060250 
003    OCoLC 
005    20200110051700.1 
006    m     o  d         
007    cr cnu|||unuuu 
008    190318s2018    gw      ob    000 0 eng d 
019    1090284897 
020    9783960677031|q(electronic book) 
020    3960677030|q(electronic book) 
020    |z9783960672036 
020    |z3960672039 
035    (OCoLC)1090060250|z(OCoLC)1090284897 
040    N$T|beng|erda|epn|cN$T|dN$T|dEBLCP|dOCLCF|dYDX|dOCLCQ 
049    RIDW 
050  4 TK7882.B56 
072  7 COM|x000000|2bisacsh 
082 04 006.4|223 
090    TK7882.B56 
100 1  Kumar, N. B. Mahesh,|eauthor. 
245 10 Finger Knuckle-Print Authentication Using Fast Discrete 
       Orthonormal Stockwell Transform /|cN.B. Mahesh Kumar, Dr. 
       K. Premalatha. 
264  1 Hamburg, Germany :|bDiplomica Verlag GmbH :|bAnchor 
       Academic Publishing,|c2018. 
300    1 online resource 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
340    |gpolychrome|2rdacc 
347    text file|2rdaft 
504    Includes bibliographical references. 
505 0  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  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  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 
520    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  Online resource; title from PDF title page (EBSCO, viewed 
       March 29, 2019). 
590    eBooks on EBSCOhost|bEBSCO eBook Subscription Academic 
       Collection - North America 
650  0 Biometric identification.|0https://id.loc.gov/authorities/
       subjects/sh2001010964 
650  7 Biometric identification.|2fast|0https://id.worldcat.org/
       fast/832607 
655  4 Electronic books. 
700 1  Premalatha, K.,|eauthor. 
776 08 |iPrint version:|aKumar, N. B. Mahesh.|tFinger Knuckle-
       Print Authentication Using Fast Discrete Orthonormal 
       Stockwell Transform.|dHamburg, Germany : Diplomica Verlag 
       GmbH : Anchor Academic Publishing, 2018|z3960672039
       |z9783960672036|w(OCoLC)1017969431 
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
       search.ebscohost.com/login.aspx?direct=true&scope=site&
       db=nlebk&AN=2070413|zOnline eBook via EBSCO. Access 
       restricted to current Rider University students, faculty, 
       and staff. 
856 42 |3Instructions for reading/downloading the EBSCO version 
       of this eBook|uhttp://guides.rider.edu/ebooks/ebsco 
901    MARCIVE 20231220 
948    |d20200122|cEBSCO|tEBSCOebooksacademic NEW 12-21,1-17 
       11948|lridw 
994    92|bRID