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Sinopsis
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.
• 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.
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Acerca del autor
Sampada Dhole has completed his PhD in Electronics from Bharati Vidyapeeth (Deemed to be University) College of Engineering, India, in 2017 with specialisation in Image Processing and Biometrics. Her research interest includes the Image Processing and Multimodal. She has published more than 30 research papers including 7 Scopus indexed. She has filed 2 patents and 1 copyright in her technical field. She has worked as a Reviewer for many International and National Conferences. She is working as Assistant Professor in the Department of E&TC at Bharati Vidyapeeth’s College of Engineering for Women, SPPU, Pune, India. She has 21 years of teaching experience. She is a member of the Technical Society ISTE, India.
Vinayak Bairagi has completed ME (Electronics) from Sinhgad COE, Pune, India, in 2007 (1st Rank in SPPU). Savitribai Phule Pune University has awarded him a PhD degree in Engineering. He has teaching experience of 13 years and research experience of 8 years. He has filed 12 patents and 5 copyrights in his technical field. He has published more than 60 papers, of which 26 papers are in international journals. He has authored/edited more than eight books/book chapters with multiple publishing concerns and he is a reviewer for nine scientific journals. He has received grants from DST SERB, UoP-BUCD, GYTI. He has received more than 14 awards, which include the National Level Young Engineer Award (2014), the ISTE National level Young Researcher Award (2015) for his excellence in the field of engineering, and IETE M N SAHA Memorial Award-2018. He is a member of INENG (UK), IETE (India), ISTE (India), and IEI & BMS (India). He had worked on Image Compression at the College of Engineering, Pune, under Pune University. His main research interests include Medical Imaging, Machine Learning, Computer-Aided Diagnosis, and Medical Signal Processing. Currently, he is associated with the AISSMS Institute of Information Technology, Pune, India, as Professor in Electronics and Telecommunication Engineering. He is a recognised PhD guide in Electronics Engineering of Savitribai Phule Pune University. Presently he is guiding seven PhD students.
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Multimodal Biometric Identification System (Paperback)
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Paperback. Condición: new. Paperback. 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. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9781032665979
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Multimodal Biometric Identification System: Case Study of Real-Time Implementation
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Condición: New. Sampada Dhole has completed his PhD in Electronics from Bharati Vidyapeeth (Deemed to be University) College of Engineering, India, in 2017 with specialisation in Image Processing and Biometrics. Her research interest includes the Image . Nº de ref. del artículo: 3381499233
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Paperback. Condición: new. Paperback. 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. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781032665979
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Multimodal Biometric Identification System (Paperback)
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Paperback. Condición: new. Paperback. 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. 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. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781032665979
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