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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 IGN] comportement [Termes IGN] détection de visage [Termes IGN] distribution spatiale [Termes IGN] données environnementales [Termes IGN] émotion [Termes IGN] entropie [Termes IGN] psychologie [Termes IGN] reconnaissance faciale [Termes IGN] sciences humaines [Termes 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, 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

Dynamic vs. Static recognition of facial expressions / Bogdan Raducanu (2008)  

Public Titre : Dynamic vs. Static recognition of facial expressions Type de document : Article/Communication Auteurs : Bogdan Raducanu, Auteur ; Fadi Dornaika , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2008 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 5355 Conférence : AmI 2008, 2nd European Conference on Ambient Intelligence 19/11/2008 22/11/2008 Nuremberg Allemagne Importance : pp 13 - 25 Format : 16 x 24 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] intelligence artificielle [Termes IGN] reconnaissance faciale [Termes IGN] robotique [Termes IGN] série temporelle Résumé : (auteur) In this paper, we address the dynamic recognition of basic facial expressions. We introduce a view- and texture independent schemes that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Furthermore, we compare this dynamic scheme with a static one and show that the former performs better than the latter. We provide evaluations of performance using several classification schemes. With the proposed scheme, we developed an application for social robotics, in which an AIBO is mirroring the facial expression recognized. Numéro de notice : C2008-032 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-540-89617-3_2 En ligne : https://doi.org/10.1007/978-3-540-89617-3_2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102615

Efficient facial expression recognition for human robot interaction / Fadi Dornaika (2007)  

Public Titre : Efficient facial expression recognition for human robot interaction Type de document : Article/Communication Auteurs : Fadi Dornaika , Auteur ; Bogdan Raducanu, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2007 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 4507 Conférence : IWANN 2007, 9th International Work-Conference on Artificial Neural Networks 20/06/2007 22/06/2007 San Sebastian Espagne Importance : pp 700 - 708 Format : 16 x 24 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] interaction homme-machine [Termes IGN] reconnaissance faciale [Termes IGN] robot [Termes IGN] vision par ordinateur Résumé : (auteur) In this paper, we propose a novel approach for facial expression analysis and recognition. The main contributions of the paper are as follows. First, we propose an efficient facial expression recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of the AIBO robot. More precisely, the robot is displaying an emotional state in response to the recognized user's facial expression. Experiments using unseen videos demonstrated the effectiveness of the developed method. Numéro de notice : C2007-061 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : 10.1007/978-3-540-73007-1_84 En ligne : https://doi.org/10.1007/978-3-540-73007-1_84 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102620