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Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model / Yizhuo Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 227 - 249 Titre : Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model Type de document : Article/Communication Auteurs : Yizhuo Li, Auteur ; Teng Fei, Auteur ; Yingjing Huang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 227 - 249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] comportement [Termes descripteurs IGN] détection de visage [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] émotion [Termes descripteurs IGN] entropie [Termes descripteurs IGN] psychologie [Termes descripteurs IGN] reconnaissance faciale [Termes descripteurs IGN] sciences humaines [Termes descripteurs IGN] vision Résumé : (auteur) Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content. Numéro de notice : A2021-037 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1755040 date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1080/13658816.2020.1755040 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96746 [article]

Machine learning and biometrics / Jucheng Yang (2018)  

Public Titre : Machine learning and biometrics Type de document : Monographie Auteurs : Jucheng Yang, Editeur scientifique ; Dong Sun Park, Editeur scientifique ; Sook Yoon, Editeur 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 descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] biométrie [Termes descripteurs IGN] exploration de données [Termes descripteurs IGN] interface homme-machine [Termes descripteurs 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 : Monographie 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