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Multimodal biometric identification system :case study of real-time implementation /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multimodal biometric identification system :Sampada Dhole and Vinayak Bairagi.
Reminder of title:	case study of real-time implementation /
Author:	Dhole, Sampada,
other author:	Bairagi, Vinayak,
Description:	x, 131 pages :illustrations ;24 cm.
Notes:	"A Chapman & Hall Book" - from cover.
Subject:	Biometric identificationCongresses.Technological innovations
ISBN:	9781032665993

Multimodal biometric identification system :case study of real-time implementation /
Dhole, Sampada,

Multimodal biometric identification system :case study of real-time implementation /Sampada Dhole and Vinayak Bairagi. - First edition. - x, 131 pages :illustrations ;24 cm.

"A Chapman & Hall Book" - from cover.

Includes bibliographical references and index.

"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 efficiently dealt with 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 & interpretation with case studies, suitable figures, tables & graphs etc. Shows that for feature level fusion using contourlet transform features with LDA for dimension reduction attains more accuracy compared to block variance features. Includes contribution in Feature Extraction & Pattern Recognition for increase in 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 biometrics applications"--

ISBN: 9781032665993

LCCN: 2024021492Subjects--Topical Terms:

445903
Biometric identification
--Technological innovations--Congresses.

LC Class. No.: TK7882.B56 / D49 2025

Dewey Class. No.: 006.2/48

Multimodal biometric identification system :case study of real-time implementation /
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245 1 0 $a Multimodal biometric identification system : $b case study of real-time implementation / $c Sampada Dhole and Vinayak Bairagi.
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264 1 $a Boca Raton, FL : $b CRC Press, $c 2025.
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300 $a x, 131 pages : $b illustrations ; $c 24 cm.
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500 $a "A Chapman & Hall Book" - from cover.
504 $a Includes bibliographical references and index.
520 $a "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 efficiently dealt with 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 & interpretation with case studies, suitable figures, tables & graphs etc. Shows that for feature level fusion using contourlet transform features with LDA for dimension reduction attains more accuracy compared to block variance features. Includes contribution in Feature Extraction & Pattern Recognition for increase in 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 biometrics applications"-- $c Provided by publisher.
588 $a Description based on online resource; title from digital title page (viewed on November 18, 2024).
650 0 $a Biometric identification $x Technological innovations $v Congresses. $3 445903
650 6 $a Identification biométrique $x Innovations $0 (CaQQLa)201-0379286 $v Éttudes de cas. $0 (CaQQLa)201-0376950 $3 1009527
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