First edition. Scope and content: "Deep Learning is now ubiquitous with applied machine learning. All of the technology giants (e.g. Google, Microsoft, Apple, etc.) are focusing on deep learning based techniques for data analytics and artificial intelligence. This technology applies quite strongly to biometrics. 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."--Provided by publisher. Cover; Half Title; Title Page; Copyright Page; Table of Contents; Editors; Contributors; 1: Deep Learning: Fundamentals and Beyond; 2: Unconstrained Face Identification and Verification Using Deep Convolutional Features; 3: Deep Siamese Convolutional Neural Networks for Identical Twins and Look-Alike Identification; 4: Tackling the Optimization and Precision Weakness of Deep Cascaded Regression for Facial Key-Point Localization; 5: Learning Deep Metrics for Person Reidentification; 6: Deep Face-Representation Learning for Kinship Verification; 7: Whatâ#x80;#x99;s Hiding in My Deep Features? 8: Stacked Correlation Filters9: Learning Representations for Unconstrained Fingerprint Recognition; 10: Person Identification Using Handwriting Dynamics and Convolutional Neural Networks; 11: Counteracting Presentation Attacks in Face, Fingerprint and Iris Recognition; 12: Fingervein Presentation Attack Detection Using Transferable Features from Deep Convolution Neural Networks; Index.