LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code needed.
Machine Learning for Biometrics
Concepts, Algorithms and Applications
- 1st Edition - January 21, 2022
- Editors: Partha Pratim Sarangi, Madhumita Panda, Subhashree Mishra, Bhabani Shankar Prasad Mishra, Banshidhar Majhi
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 8 5 2 0 9 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 3 3 9 - 4
Machine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometric… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteMachine Learning for Biometrics: Concepts, Algorithms and Applications highlights the fundamental concepts of machine learning, processing and analyzing data from biometrics and provides a review of intelligent and cognitive learning tools which can be adopted in this direction. Each chapter of the volume is supported by real-life case studies, illustrative examples and video demonstrations. The book elucidates various biometric concepts, algorithms and applications with machine intelligence solutions, providing guidance on best practices for new technologies such as e-health solutions, Data science, Cloud computing, and Internet of Things, etc.
In each section, different machine learning concepts and algorithms are used, such as different object detection techniques, image enhancement techniques, both global and local feature extraction techniques, and classifiers those are commonly used data science techniques. These biometrics techniques can be used as tools in Cloud computing, Mobile computing, IOT based applications, and e-health care systems for secure login, device access control, personal recognition and surveillance.
- Covers different machine intelligence concepts, algorithms and applications in the field of cybersecurity, e-health monitoring, secure cloud computing and secure IOT based operations
- Explores advanced approaches to improve recognition performance of biometric systems with the use of recent machine intelligence techniques
- Introduces detection or segmentation techniques to detect biometric characteristics from the background in the input sample
Faculty members, graduate/master degree students and research scholars, practitioners, developers, engineers, etc. from Computer Science and Engineering, Information Technology, Electronics Engineering, Electrical Engineering, Electrical and Electronics Engineering disciplines. Biometrics, Computer vision, Data Science, Cloud computing, Cyber Security, e-Health monitoring systems, Internet of Things security
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Acknowledgments
- Chapter 1: Machine learning approach for longitudinal face recognition of children
- Abstract
- 1: Introduction
- 2: Face modality for children face recognition
- 3: Children face databases
- 4: Face recognition of children
- 5: Feature extraction techniques
- 6: Machine learning classification methods
- 7: Deep learning approach
- 8: Results
- 9: Discussion and conclusion
- Chapter 2: Thermal biometric face recognition (TBFR): A noncontact face biometry
- Abstract
- 1: Introduction
- 2: Existing approaches
- 3: Proposed work
- 4: Experimental results
- 5: Conclusion
- Chapter 3: Multimodal biometric recognition using human ear and profile face: An improved approach
- Abstract
- 1: Introduction
- 2: Related work
- 3: Background
- 4: Proposed multimodal biometric recognition
- 5: Experiments and results
- 6: Conclusion
- Chapter 4: Statistical measures for Palmprint image enhancement
- Abstract
- 1: Introduction
- 2: Palmprint image enhancement
- 3: Statistical measure
- 4: Discussion and analysis
- 5: Conclusion
- Chapter 5: Retina biometrics for personal authentication
- Abstract
- 1: Introduction
- 2: Framework of authentication system using biometrics
- 3: Related work
- 4: Anatomy of retina
- 5: ANFIS-based retina biometric authentication system (ARBAS)
- 6: Conclusion
- Chapter 6: Gender recognition from facial images using multichannel deep learning framework
- Abstract
- 1: Introduction
- 2: Literature review
- 3: Preliminaries
- 4: Proposed system
- 5: Shallow CNN
- 6: Feature fusion
- 7: Experimental results and discussion
- 8: Single-channel approach
- 9: Proposed MC-DLF approach
- 10: Summary
- Chapter 7: Implementation of cardiac signal for biometric recognition from facial video
- Abstract
- 1: Introduction
- 2: Biometric identification system based on facial video
- 3: Results and discussions
- 4: Conclusions and future work
- Chapter 8: Real-time emotion engagement tracking of students using human biometric emotion intensities
- Abstract
- 1: Introduction
- 2: Related work
- 3: Methodology
- 4: Results and discussions
- 5: Conclusions
- Chapter 9: Facial identification expression-based attendance monitoring and emotion detection—A deep CNN approach
- Abstract
- 1: Introduction
- 2: Related work
- 3: Proposed work
- 4: Conclusion and future work
- Chapter 10: Contemporary survey on effectiveness of machine and deep learning techniques for cyber security
- Abstract
- 1: Introduction
- 2: Biometrics in cyber security
- 3: Cyber attacks versus machine learning
- 4: Applications of ML and DL in cyber security
- 5: Machine learning-based cyber security systems
- 6: Deep learning-based cyber security systems
- 7: Conclusion and future scope
- Chapter 11: A secure biometric authentication system for smart environment using reversible data hiding through encryption scheme
- Abstract
- 1: Introduction
- 2: Literature review
- 3: Proposed scheme
- 4: Illustrative examples
- 5: Experimental study and result analysis
- 6: Conclusion
- Chapter 12: An efficient and untraceable authentication protocol for cloud-based healthcare system
- Abstract
- 1: Introduction
- 2: Related work
- 3: System model and security goals
- 4: Proposed protocol
- 5: Security analysis
- 6: Performance analysis
- 7: Conclusion
- Index
- No. of pages: 264
- Language: English
- Edition: 1
- Published: January 21, 2022
- Imprint: Academic Press
- Paperback ISBN: 9780323852098
- eBook ISBN: 9780323903394
PS
Partha Pratim Sarangi
Dr. Partha Pratim Sarangi, is working as Professor in Computer Sc. & Engineering at Seemanta Engineering College, Jharpokharia, Odisha, INDIA. He has received his Ph.D. in Computer Science from KIIT University, Bhubaneswar, Odisha. His current research interests include Pattern Recognition, Soft computing, Biometrics, Computer Vision, and Data Science. He has already published about 22 research papers in refereed journals and conferences.
Affiliations and expertise
Professor in Computer Science and Engineering, Seemanta Engineering College, Jharpokharia, Baripada, Odisha, IndiaMP
Madhumita Panda
Ms. Madhumita Panda, is working as Assistant Professor in Master in Computer Applications at Seemanta Engineering College, Jharpokharia, Odisha. She has received his M.Tech in Computer Science from NIT Rourkela and continuing her Ph.D. from North Orissa University, Baripada, Odisha. Her current research interests include Pattern Recognition, Soft computing, Biometrics, Software Engineering. She has already published 2 book chapters and about 10 research papers in refereed journals and conferences.
Affiliations and expertise
Assistant Professor, Master in Computer Applications, Seemanta Engineering College, Jharpokharia, Baripada, Odisha, IndiaSM
Subhashree Mishra
Dr. Subhashree Mishra, is working as an Assistant Professor in School of Electronics Engineering at KIIT University, Bhubaneswar. She has completed her Master and PhD from KIIT University respectively. Her research interest includes Machine Learning, Communication Engineering, Image Processing and Soft Computing. She has published around 15 research articles and 4 Book chapters in referred journals and conferences. Currently she is guiding 2 PhD scholars.
Affiliations and expertise
Assistant Professor, School of Electronics Engineering, KIIT University, Bhubaneswar, Odisha, IndiaBM
Bhabani Shankar Prasad Mishra
Dr. Bhabani Shankar Prasad Mishra born in Talcher, Odisha, India in 1981. He received the B.Tech. in Computer Science and Engineering from Biju Pattanaik Technical University, Odisha in 2003, M.Tech. degree in Computer Science and Engineering from the KIIT University, in 2003, Ph.D. degree in Computer Science from F.M.University, Balasore,Odisha, India, in 2011 and Post Doc in 2013 from Soft Computing Laboratory, Yansei University, South Korea .
Currently he is working as an Associate Professor and Dean at School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. His research interest includes Pattern Reorganization, Data Mining, Soft Computing, Big Data and Machine Learning. He has published more than 80 research articles in reputed Journal and Conferences, has edited more than five books of current importance. Under his guidance, 2 PhD scholars are already been awarded;
Dr. Mishra was the recipient of the Gold Medal and Silver Medal during his M.Tech for the best Post Graduate in the University. He is the member of different technical bodies ISTE, CSI and IET.
Affiliations and expertise
Associate Professor, School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India
Dean, School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, IndiaBM
Banshidhar Majhi
Prof. Banshidhar Majhi has three years of industry experience and more than 28 years of teaching and research experience in the field of Computer Science and Engineering. He is associated with NIT Rourkela since 1991 and presently serving as the Director IIITDM since July 2017. He has served in various administrative positions as HOD, Dean (Academic), Chairman, Automation Cell. He has been serving as members of various accreditation committees like the NBA and NAAC.
He has guided 17 Ph.D. scholars and 8 MS (research) students in addition to more than 150 M. Tech. theses. He has 80 research publications in peer reviewed journals and more than 150 publications in conferences of repute. He is a Senior Member IEEE, Fellow IETE, Fellow IE (India), Life Member of Computer Society of India. For his outstanding contributions in Engineering and Technology, Govt. of Odisha has conferred on him “Samanta Chandra Sekhar” award in 2016. Prof. Majhi puts his best effort whatever he does and believes in terminology “Efforts never Fail”.
As the Director, Prof. Majhi’s single point agenda is to make IIITDM as a centre of excellence in design centric engineering education and make it as a destination for quality students and faculty to achieve their potential.
Affiliations and expertise
Director, IIITDM, Kancheepuram, Kancheepuram, Chennai, IndiaRead Machine Learning for Biometrics on ScienceDirect