Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start-ups are focusing on deep learning-based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. This book covers topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoencoders. The focus is also on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints, while examining the future trends in deep learning and biometric research.
- Contains chapters written by authors who are leading researchers in biometrics.
- Presents a comprehensive overview on the internal mechanisms of deep learning.
- Discusses the latest developments in biometric research.
- Examines future trends in deep learning and biometric research.
- Provides extensive references at the end of each chapter to enhance further study.
Table of Contents
Introduction to Deep Learning. Fast Deep Learning Architechtures. Multispectral Face Recognition with Deep Learning. Deep Matric Learning. Unconstrained Face Recognition with Deep Learning. 3D Face Processing with Deep Learning. Kinship Recognition with Deep Learning. Ocular Recognition with Deep Learning. Fingerprint Recognition with Deep Learning. Multispecteal iris Recognition with Deep Learning.
Mayank Vatsa is an Associate Professor at IIIT New Delhi. He has authored more than 150 publications dealing with biometrics, image processing, machine learning and information fusion. He is a Senior Member of IEEE.
Richa Singh is an Associate Professor at IIIT New Delhi. She has authored over 100 publications on biometrics, patter recognition and machine learning in referred journals, book chapters and conferences.
Angshul Majumdar is an Assistant Professor at IIIT New Delhi. He is an active research in biomimetics and machine learning.