AI and Deep Learning in Biometric Security (eBook, PDF)

Trends, Potential, and Challenges
Redaktion: Jaswal, Gaurav; Ramachandra, Raghavendra; Kanhangad, Vivek

• Format: PDF

Produktbeschreibung

This book focuses on artificial intelligence and deep learning approaches, with case studies to solve problems associated with biometric security such as authentication, spoofing attack detection, and object detection.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 378
• Erscheinungstermin: 21. März 2021
• Englisch
• ISBN-13: 9781000291629
• Artikelnr.: 61131625

Autorenporträt

Dr. Gaurav Jaswal is currently working as Project Scientist, Electrical Engineering at National Agri-Food Biotechnology Institute Mohali. Prior to this, he was Research Associate, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, India. He received M.Tech and Ph.D degree in Electrical Engineering from National Institute of Technology Hamirpur in 2018. His research interests are in the areas of multimodal biometrics, biomedical signal processing and deep learning. He regularly reviews papers for various international journals including IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM), IET Biometrics. Dr. Vivek Kanhangad is currently working as Associate Professor, Department of Electrical Engineering, Indian Institute of Technology Indore since Feb, 2012. Prior to this, he was Visiting Assistant Professor, International Institute of Information Technology Bangalore (Jun 2010-Dec 2012). He received Ph.D. from the Hong Kong Polytechnic University in 2010. Prior to joining Hong Kong PolyU, he received M.Tech. degree in Electrical Engineering from Indian Institute of Technology Delhi, in 2006 and worked for Motorola India Electronics Ltd, Bangalore for a while. His research interests are in the overlapping areas of digital signal and image processing, pattern recognition with focus on biometrics and biomedical applications. He regularly reviews papers for various international journals including IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Cybernetics, IEEE Transactions on Human-Machine Systems and Elsevier journals - Pattern Recognition and Pattern Recognition Letters. Dr. Raghavendra Ramachandra is currently working as a Professor in Department of Information Security and Communication Technology (IIK). He is member of Norwegian Biometrics Laboratory at NTNU Gjøvik. He received B.E (Electronics and Communication) from University of Mysore, India. M.Tech (Digital Electronics and Advance Communication Systems) from Visvesvaraya Technological University, India. Ph.D. (Computer Science with specialization of Pattern Recognition and Image Processing) from the University of Mysore, India and Telcom SudParis, France. His research interest includes Pattern Recognition, Image and video analytics, Biometrics, Human Behaviour Analysis, Video Surviellance, Health Biometrics, and Smartphone Authentication.

Inhaltsangabe

1. Deep Learning-Based Hyperspectral Multimodal Biometric Authentication
System Using Palmprint and Dorsal Hand Vein. 2. Cancelable Biometrics for
Template Protection: Future Directives with Deep Learning. 3. On Training
Generative Adversarial Network for Enhancement of Latent Fingerprints. 4.
DeepFake Face Video Detection Using Hybrid Deep Residual Networks nad LSTM
Architecture. 5. Multi-spectral Short-Wave Infrared Sensors and
Convolutional Neural Networks for Biometric Presentation Attack Detection.
6. AI-Based Approach for Person Identification Using ECG Biometric. 7.
Cancelable Biometric Systems from Research to Reality: The Road Less
Travelled. 8. Gender Classification under Eyeglass Occluded Ocular Region:
An Extensive Study Using Multi-spectral Imaging. 9. Investigation of the
Fingernail Plate for Biometric Authentication using Deep Neural Networks.
10. Fraud Attack Detection in Remote Verification systems for Non-enrolled
Users. 11. Indexing on Biometric Databases. 12. Iris Segmentation in the
Wild Using Encoder-Decoder-Based Deep Learning Techniques. 13. PPG-Based
Biometric Recognition: Opportunities with Machine and Deep Learning. 14.
Current Trends of Machine Learning Techniques in Biometrics and its
Applications.