Fr. 88.00

Multibiometrics Systems: Modern Perspectives to Identity Verification - Secure Multimodal Biometric Systems

English · Paperback / Softback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

In recent years, use of biometric systems has been increasing rapidly in the field of identity verification of individuals. Human physiological or behavioral characteristics are available uniquely to each individual as biometrics evidence. Unimodal biometrics systems are designed based on a single identifier, such as face, fingerprint, etc. However, it suffers from several limitations and due to these limitations it often degrades the performance. These limitations can be minimized by using multibiometric systems that consolidate evidences obtained from multiple biometrics sources. The overall aim of this book is to present some novel multibiometrics systems which can be used in different security related applications. Further, this book is an invaluable collection of most up-to-date multibiometrics algorithms which are presented with novel computer vision and machine learning techniques under a unified framework. This book will be helpful to industry practitioners, researchers and academicians who are engaged in various disciplines of system sciences, information security and identity businesses.

Product details

Authors Phalgun Gupta, Phalguni Gupta, Dakshina Ranja Kisku, Dakshina Ranjan Kisku, Tistarelli, Massimo Tistarelli
Publisher LAP Lambert Academic Publishing
 
Languages English
Product format Paperback / Softback
Released 01.01.2015
 
EAN 9783848449378
ISBN 978-3-8484-4937-8
No. of pages 176
Subjects Guides
Natural sciences, medicine, IT, technology > IT, data processing

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.