Deep Learning for Biometrics

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Artificial Intelligence, General Computing
Cover of the book Deep Learning for Biometrics by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319616575
Publisher: Springer International Publishing Publication: August 1, 2017
Imprint: Springer Language: English
Author:
ISBN: 9783319616575
Publisher: Springer International Publishing
Publication: August 1, 2017
Imprint: Springer
Language: English

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.

Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits  deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories.

Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation for perceptual and biometrics-related tasks. The text offers a showcase of cutting-edge research on the use of convolutional neural networks (CNN) in face, iris, fingerprint, and vascular biometric systems, in addition to surveillance systems that use soft biometrics. Issues of biometrics security are also examined.

Topics and features: addresses the application of deep learning to enhance the performance of biometrics identification across a wide range of different biometrics modalities; revisits  deep learning for face biometrics, offering insights from neuroimaging, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent fingerprint and finger-vein recognition, as well as iris recognition; discusses deep learning for soft biometrics, including approaches for gesture-based identification, gender classification, and tattoo recognition; investigates deep learning for biometrics security, covering biometrics template protection methods, and liveness detection to protect against fake biometrics samples; presents contributions from a global selection of pre-eminent experts in the field representing academia, industry and government laboratories.

Providing both an accessible introduction to the practical applications of deep learning in biometrics, and a comprehensive coverage of the entire spectrum of biometric modalities, this authoritative volume will be of great interest to all researchers, practitioners and students involved in related areas of computer vision, pattern recognition and machine learning.

More books from Springer International Publishing

Cover of the book A Practical Approach to Adolescent Bone Health by
Cover of the book Virtual Reality and Augmented Reality by
Cover of the book Fear and Uncertainty in Europe by
Cover of the book Operations Research and Enterprise Systems by
Cover of the book Brand Building and Marketing in Key Emerging Markets by
Cover of the book Luigi L. Pasinetti: An Intellectual Biography by
Cover of the book Management of the Fuzzy Front End of Innovation by
Cover of the book Hyper-lattice Algebraic Model for Data Warehousing by
Cover of the book Developing Disaster Resilient Housing in Vietnam: Challenges and Solutions by
Cover of the book Time Series Analysis and Its Applications by
Cover of the book Youth and Justice in Western States, 1815-1950 by
Cover of the book Case-Based Reasoning Research and Development by
Cover of the book Orienting Feminism by
Cover of the book Antitrust Analysis of Online Sales Platforms & Copyright Limitations and Exceptions by
Cover of the book Ad Hoc Networks by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy