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Machine learning and biometrics / Jucheng Yang (2018)  

Public Titre : Machine learning and biometrics Type de document : Monographie Auteurs : Jucheng Yang, Éditeur scientifique ; Dong Sun Park, Éditeur scientifique ; Sook Yoon, Éditeur scientifique ; et al., Auteur Editeur : London [UK] : IntechOpen Année de publication : 2018 Importance : 146 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-1-83881-556-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] biométrie [Termes IGN] exploration de données [Termes IGN] interface homme-machine [Termes IGN] reconnaissance faciale Résumé : (éditeur) We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: ""Biometrics"" and ""Machine Learning for Biometrics."" Parts I and II contain four and three chapters, respectively. The book is reviewed by editors: Prof. Jucheng Yang, Prof. Dong Sun Park, Prof. Sook Yoon, Dr. Yarui Chen, and Dr. Chuanlei Zhang. Note de contenu : 1- Introductory chapter: machine learning and biometrics 2- Recognition of eye characteristics 3- A survey on soft biometrics for human identification 4- Face recognition with facial occlusion based on local cycle graph structure operator 5- Electrocardiogram recognization based on variational autoEncoder 6- A survey on methods of image processing and recognition for personal identification 7- A human body mathematical model biometric using golden ratio: A new algorithm Numéro de notice : 28505 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.71297 En ligne : https://doi.org/10.5772/intechopen.71297 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97028