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Author Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya edit  openurl
  Title Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” Type Journal
  Year 2018 Publication Informaciones Psiquiatricas Abbreviated Journal  
  Volume 232 Issue Pages 47-59  
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  ISSN 0210-7279 ISBN Medium  
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  Notes HUPBA; no menciona Approved no  
  Call Number (up) Admin @ si @ FAA2018 Serial 3214  
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Author Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari edit  doi
openurl 
  Title Integrating Vision and Language for First Impression Personality Analysis Type Journal Article
  Year 2018 Publication IEEE Multimedia Abbreviated Journal MULTIMEDIA  
  Volume 25 Issue 2 Pages 24 - 33  
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  Abstract The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness.  
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  Notes HUPBA; 602.133 Approved no  
  Call Number (up) Admin @ si @ GAL2018 Serial 3124  
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Author Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier edit  doi
openurl 
  Title Multimodal First Impression Analysis with Deep Residual Networks Type Journal Article
  Year 2018 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 8 Issue 3 Pages 316-329  
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  Abstract People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations  
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  Notes HUPBA; no proj Approved no  
  Call Number (up) Admin @ si @ GGB2018 Serial 3210  
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Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla edit  doi
openurl 
  Title Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
  Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications  
  Volume 28 Issue 5 Pages 853–854  
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  Notes HuPBA;MILAB; no menciona Approved no  
  Call Number (up) Admin @ si @ GGE2017 Serial 2845  
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Author Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari edit  doi
openurl 
  Title Dominant and Complementary Emotion Recognition from Still Images of Faces Type Journal Article
  Year 2018 Publication IEEE Access Abbreviated Journal ACCESS  
  Volume 6 Issue Pages 26391 - 26403  
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  Abstract Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.  
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  Notes HUPBA; no proj Approved no  
  Call Number (up) Admin @ si @ GLW2018 Serial 3122  
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