Deep Learning in Biometrics
Coordonnateurs : Vatsa Mayank, Singh Richa, Majumdar Angshul
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.
- Contains chapters written by authors who are leading researchers in biometrics.
- Presents a comprehensive overview on the internal mechanisms of deep learning.
- Discusses the latest developments in biometric research.
- Examines future trends in deep learning and biometric research.
- Provides extensive references at the end of each chapter to enhance further study.
Mayank Vatsa is an Associate Professor at IIIT New Delhi. He has authored more than 150 publications dealing with biometrics, image processing, machine learning and information fusion. He is a Senior Member of IEEE.
Richa Singh is an Associate Professor at IIIT New Delhi. She has authored over 100 publications on biometrics, patter recognition and machine learning in referred journals, book chapters and conferences.
Angshul Majumdar is an Assistant Professor at IIIT New Delhi. He is an active research in biomimetics and machine learning.
Date de parution : 06-2024
15.6x23.4 cm
Date de parution : 03-2018
15.6x23.4 cm
Thèmes de Deep Learning in Biometrics :
Mots-clés :
Roc; Sparse Autoencoder; Deep Metric Learning; Face Identification Task; 3D Processing with Deep Learning; Data Set; Multispectral Iris Recognition; LBP; Ocular Recognition; Convolutional Layers; Face Recognition; CASIA Data Set; Metric Learning; Softmax Classifier; Shruti Nagpal; Roc Curve; Maneet Singh; Local Binary Pattern; Richa Singh; Stochastic Gradient Descent; Jun-Cheng Chen; Deep CNN; Rajeev Ranjan; Presentation Attack; Vishal M; Patel; Deep Neural Network; Carlos D; Castillo; Biometric Modalities; Rama Chellappa; CNN Architecture; Xiaoxia Sun; Face Images; Amirsina Torfi; Latent Fingerprints; Nasser Nasrabadi; Pedestrian Data; Yuhang Wu; Kinship Verification; Shishir K; Shah; Correlation Output; Ioannis A; Kakadiaris; RBM; Hailin Shi; Score Maps; Shengcai Liao; DBN; Dong Yi; Face Detection; Stan Z; Li; Naman Kohli; Daksha Yadav; Afzel Noore; Ethan M; Rudd; Manuel Günther; Akshay R; Dhamija; Faris A; Kateb; Terrance E; Boult; Jonathon M; Smereka; Vishnu Naresh Boddeti; B; V; K; Vijaya Kumar; Aakarsh Malhotra; Anush Sankaran; Gustavo H; Rosa; João P; Papa; Walter J; Scheirer; Allan Pinto; Helio Pedrini; Michael Krumdick; Benedict Becker; Adam Czajka; Kevin W; Bowyer; Anderson Rocha; Raghavendra Ramachandra; Kiran B; Raja; Sushma Venkatesh; Christoph Busch