Название: Multimodal Biometric Identification System: Case Study of Real-Time Implementation
Автор: Sampada Dhole, Vinayak Bairagi
Издательство: CRC Press
Год: 2025
Страниц: 142
Язык: английский
Формат: pdf (true), epub
Размер: 26.6 MB

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.

One of the primary concerns in the present day of information technology is the accessible and widespread availability of information that needs to be secure. Because the integrity and confidentiality of information are vital, it must be protected from unauthorised access. The term “security” refers to preventing (unauthorised) access to certain crucial information or valuables. For this reason, a variety of applications, including those for ATMs, driving licenses, passports, citizen cards, cell phones, and voter ID cards, require precise, automatic personal identification. Apart from identity, security holds equal significance. Passwords and PINs, which were utilised for identification and validation in previous decades, are unreliable due to the likelihood of fraud. Using biometric identification provides a solution to this kind of issue.

Biometric systems use behavioural or physical characteristics to identify individuals. The fingerprint, face, iris, retina, palmprint, speech pattern, signature, gait, and many other characteristics are most often utilised in biometric systems. The two main uses of biometric systems are authentication and identification. A biometric system has various benefits over conventional tech- niques. Biometric characteristics are not lost or forgotten, unlike tokens and passwords. Biometric characteristics are difficult to duplicate, share, transfer, or steal. A biometric system functions as a pattern recognition system by collecting biometric data from each user, extracting a feature set from the collected data, and comparing the feature set with a template that has been stored in the database.

• 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.