Deep learning in biometrics /
edited by Mayank Vatsa, Richa Singh and Angshul Majumdar.
- First edition.
- 1 online resource (328 pages) : 153 illustrations
chapter 1 Deep Learning: Fundamentals and Beyond / chapter 2 Unconstrained Face Identification and Verification Using Deep Convolutional Features / chapter 3 Deep Siamese Convolutional Neural Networks for Identical Twins and Look-Alike Identification / chapter 4 Tackling the Optimization and Precision Weakness of Deep Cascaded Regression for Facial Key-Point Localization / chapter 5 Learning Deep Metrics for Person Reidentification / chapter 6 Deep Face-Representation Learning for Kinship Verification / chapter 7 What's Hiding in My Deep Features? / chapter 8 Stacked Correlation Filters / chapter 9 Learning Representations for Unconstrained Fingerprint Recognition / chapter 10 Person Identification Using Handwriting Dynamics and Convolutional Neural Networks / chapter 11 Counteracting Presentation Attacks in Face, Fingerprint, and Iris Recognition / chapter 12 Fingervein Presentation Attack Detection Using Transferable Features from Deep Convolution Neural Networks / Shruti Nagpal -- Jun-Cheng Chen * -- Xiaoxia Sun -- Yuhang Wu -- Hailin Shi -- Naman Kohli -- Ethan M. Rudd -- Jonathon M. Smereka -- Aakarsh Malhotra -- Gustavo H. Rosa -- Allan Pinto -- Raghavendra Ramachandra.
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.
9781351265003
Biometric Identification.
Machine Learning.
Biometric Identification.
Machine Learning.
SCIENCE--Life Sciences--General.
TECHNOLOGY & ENGINEERING--Electronics--General.
COMPUTERS / Machine Theory.
TECHNOLOGY & ENGINEERING / Imaging Systems.
Deep Metric Learning.
Face Recognition.
Metric Learning.
Multispectral Iris Recognition.
Ocular Recognition.
3D Processing With Deep Learning.
TK7882.B56
570.1/5195
W 786
chapter 1 Deep Learning: Fundamentals and Beyond / chapter 2 Unconstrained Face Identification and Verification Using Deep Convolutional Features / chapter 3 Deep Siamese Convolutional Neural Networks for Identical Twins and Look-Alike Identification / chapter 4 Tackling the Optimization and Precision Weakness of Deep Cascaded Regression for Facial Key-Point Localization / chapter 5 Learning Deep Metrics for Person Reidentification / chapter 6 Deep Face-Representation Learning for Kinship Verification / chapter 7 What's Hiding in My Deep Features? / chapter 8 Stacked Correlation Filters / chapter 9 Learning Representations for Unconstrained Fingerprint Recognition / chapter 10 Person Identification Using Handwriting Dynamics and Convolutional Neural Networks / chapter 11 Counteracting Presentation Attacks in Face, Fingerprint, and Iris Recognition / chapter 12 Fingervein Presentation Attack Detection Using Transferable Features from Deep Convolution Neural Networks / Shruti Nagpal -- Jun-Cheng Chen * -- Xiaoxia Sun -- Yuhang Wu -- Hailin Shi -- Naman Kohli -- Ethan M. Rudd -- Jonathon M. Smereka -- Aakarsh Malhotra -- Gustavo H. Rosa -- Allan Pinto -- Raghavendra Ramachandra.
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.
9781351265003
Biometric Identification.
Machine Learning.
Biometric Identification.
Machine Learning.
SCIENCE--Life Sciences--General.
TECHNOLOGY & ENGINEERING--Electronics--General.
COMPUTERS / Machine Theory.
TECHNOLOGY & ENGINEERING / Imaging Systems.
Deep Metric Learning.
Face Recognition.
Metric Learning.
Multispectral Iris Recognition.
Ocular Recognition.
3D Processing With Deep Learning.
TK7882.B56
570.1/5195
W 786