Deep learning in biometrics

"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.

Deep learning : fundamentals and beyond / Shruti Nagpal, Maneet Singh, Mayank Vatsa, and Richa Singh -- Unconstrained face identification and verification using deep convolutional features / Jun-Cheng Chen, Rajeev Ranjan, Vishal M. Patel, Carlos D. Castillo, and Rama Chellappa -- Deep Siamese convolutional neural networks for identical twins and look-alike identification / Xiaoxia Sun, Amirsina Torfi, and Nasser Nasrabadi -- Tackling the optimization and precision weakness of deep cascaded regression for facial key-point localization / Yuhang Wu, Shishir K. Shah, and Ioannis A. Kakadiaris -- Learning deep metrics for person re-identification / Hailin Shi, Shengcai Liao, Dong Yi, and Stan Z. Li -- Deep face representation learning for kinship verification / Naman Kohli, Daksha Yadav, Mayank Vatsa, Richa Singh, and Afzel Noore -- What's hiding in my deep features? / Ethan M. Rudd, Manuel Gunther, Akshay R. Dhamija, Faris A. Kateb, and Terrance E. Boult -- Stacked correlation filters / Jonathon M. Smereka, Vishnu Naresh Boddeti, and B.V.K. Vijaya Kumar -- Learning representations for unconstrained fingerprint recognition / Aakarsh Malhotra, Anush Sankaran, Mayank Vatsa, and Richa Sin…