AI and deep learning in biometric security : trends, potential, and challenges / edited by Gaurav Jaswal, Vivek Kanhangad, and Raghavendra Ramachandra.
Tipo de material: TextoSeries Artificial intelligence (AI). Elementary to advanced practicesDetalles de publicación: Boca Raton : CRC Press, 2021 Descripción: XIII, 364 p. ; 24 cmISBN: 9780367422448Tema(s): Aprendizaje automático (Inteligencia artificial) | Biometría | Seguridad informáticaResumen: This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc.This text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions.This book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.Tipo de ítem | Biblioteca de origen | Signatura | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
---|---|---|---|---|---|---|
Monografías | 03. BIBLIOTECA INGENIERÍA PUERTO REAL | 681.3/JAS/aia (Navegar estantería(Abre debajo)) | Prestado | 31/01/2025 | 3745135263 |
Bibliografía. - índice.
This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc.This text highlights a showcase of cutting-edge research on the use of convolution neural networks, autoencoders, recurrent convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to understanding deep learning concepts for biometrics authentication approaches and presents an analysis of biometric images under various environmental conditions.This book is sure to catch the attention of scholars, researchers, practitioners, and technology aspirants who are willing to research in the field of AI and biometric security.
No hay comentarios en este titulo.