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.
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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.
This book will cover all the topics in deep learning, namely convolutional neural networks, deep belief network and stacked autoenders. The focus will be on the application of these techniques to various biometric modalities: face, iris, palmprint, and fingerprints.
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