Deep Learning for Biometrics

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. Dr. Bir Bhanu is Bourns Presidential Chair, DistinguishedProfessor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video. Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.

Auteur

Dr. Bir Bhanu is Bourns Presidential Chair, Distinguished Professor of Electrical and Computer Engineering and the Director of the Center for Research in Intelligent Systems at the University of California at Riverside, USA. Some of his other Springer publications include the titles Video Bioinformatics, Distributed Video Sensor Networks, and Human Recognition at a Distance in Video.

Dr. Ajay Kumar is an Associate Professor in the Department of Computing at the Hong Kong Polytechnic University.

Contenu

Part I: Deep Learning for Face Biometrics.- The Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep Learning.- Real-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing Forest.- CMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face Detection.- Part II: Deep Learning for Fingerprint, Fingervein and Iris Recognition.- Latent Fingerprint Image Segmentation Using Deep Neural Networks.- Finger Vein Identification Using Convolutional Neural Network and Supervised Discrete Hashing.- Iris Segmentation Using Fully Convolutional Encoder-Decoder Networks.- Part III: Deep Learning for Soft Biometrics.- Two-Stream CNNs for Gesture-Based Verification and Identification: Learning User Style.- DeepGender2: A Generative Approach Toward Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Attention Shift Convolutional Neural Networks (PTAS-CNN) and Deep Convolutional Generative Adversarial Networks (DCGAN).- Gender Classification from NIR Iris Images Using Deep Learning.- Deep Learning for Tattoo Recognition.- Part IV: Deep Learning for Biometric Security and Protection.- Learning Representations for Cryptographic Hash Based Face Template Protection.- Deep Triplet Embedding Representations for Liveness Detection.