chapter 1 Deep Learning: Fundamentals and Beyond / Shruti Nagpal -- chapter 2 Unconstrained Face Identification and Verification Using Deep Convolutional Features / Jun-Cheng Chen * -- chapter 3 Deep Siamese Convolutional Neural Networks for Identical Twins and Look-Alike Identification / Xiaoxia Sun -- chapter 4 Tackling the Optimization and Precision Weakness of Deep Cascaded Regression for Facial Key-Point Localization / Yuhang Wu -- chapter 5 Learning Deep Metrics for Person Reidentification / Hailin Shi -- chapter 6 Deep Face-Representation Learning for Kinship Verification / Naman Kohli -- chapter 7 What's Hiding in My Deep Features? / Ethan M. Rudd -- chapter 8 Stacked Correlation Filters / Jonathon M. Smereka -- chapter 9 Learning Representations for Unconstrained Fingerprint Recognition / Aakarsh Malhotra -- chapter 10 Person Identification Using Handwriting Dynamics and Convolutional Neural Networks / Gustavo H. Rosa -- chapter 11 Counteracting Presentation Attacks in Face, Fingerprint, and Iris Recognition / Allan Pinto -- chapter 12 Fingervein Presentation Attack Detection Using Transferable Features from Deep Convolution Neural Networks / 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.