Deep Learning for Biometrics

TRAMA

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.

SOMMARIO

Part I: Deep Learning for Face BiometricsThe Functional Neuroanatomy of Face Processing: Insights from Neuroimaging and Implications for Deep LearningKalanit Grill-Spector, Kendrick Kay and Kevin S. WeinerReal-Time Face Identification via Multi-Convolutional Neural Network and Boosted Hashing ForestYuri Vizilter, Vladimir Gorbatsevich, Andrey Vorotnikov and Nikita KostromovCMS-RCNN: Contextual Multi-Scale Region-Based CNN for Unconstrained Face DetectionChenchen Zhu, Yutong Zheng, Khoa Luu and Marios SavvidesPart II: Deep Learning for Fingerprint, Fingervein and Iris RecognitionLatent Fingerprint Image Segmentation Using Deep Neural NetworksJude Ezeobiejesi and Bir BhanuFinger Vein Identification Using Convolutional Neural Network and Supervised Discrete HashingCihui Xie and Ajay KumarIris Segmentation Using Fully Convolutional Encoder-Decoder NetworksEhsaneddin Jalilian and Andreas UhlPart III: Deep Learning for Soft BiometricsTwo-Stream CNNs for Gesture-Based Verification and Identification: Learning User StyleJonathan Wu, Jiawei Chen, Prakash Ishwar and Janusz KonradDeepGender2: 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)Felix Juefei-Xu, Eshan Verma and Marios SavvidesGender Classification from NIR Iris Images Using Deep LearningJuan Tapia and Carlos AravenaDeep Learning for Tattoo RecognitionXing Di and Vishal M. PatelPart IV: Deep Learning for Biometric Security and ProtectionLearning Representations for Cryptographic Hash Based Face Template ProtectionRohit Kumar Pandey, Yingbo Zhou, Bhargava Urala Kota and Venu GovindarajuDeep Triplet Embedding Representations for Liveness DetectionFederico Pala and Bir Bhanu

AUTORE

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.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783319871288
• Collana: Advances in Computer Vision and Pattern Recognition
• Dimensioni: 235 x 155 mm Ø 5838 gr
• Formato: Brossura
• Illustration Notes: XXXI, 312 p. 117 illus., 96 illus. in color.
• Pagine Arabe: 312
• Pagine Romane: xxxi