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Author Bogdan Raducanu; Fadi Dornaika edit   pdf
doi  openurl
  Title Texture-independent recognition of facial expressions in image snapshots and videos Type Journal Article
  Year 2013 Publication Machine Vision and Applications Abbreviated Journal (down) MVA  
  Volume 24 Issue 4 Pages 811-820  
  Keywords  
  Abstract This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme 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. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes OR; 600.046; 605.203;MV Approved no  
  Call Number Admin @ si @ RaD2013 Serial 2230  
Permanent link to this record
 

 
Author Bogdan Raducanu; D. Gatica-Perez edit   pdf
doi  openurl
  Title Inferring competitive role patterns in reality TV show through nonverbal analysis Type Journal Article
  Year 2012 Publication Multimedia Tools and Applications Abbreviated Journal (down) MTAP  
  Volume 56 Issue 1 Pages 207-226  
  Keywords  
  Abstract This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status, and predicting the fired candidates. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1380-7501 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaG2012 Serial 1360  
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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu edit   pdf
openurl 
  Title Estado del Arte en Sistemas de Vision Artificial para Personas Invidentes Type Journal
  Year 2013 Publication Komputer Sapiens Abbreviated Journal (down) KS  
  Volume 1 Issue Pages 20-25  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ TSR2013 Serial 2231  
Permanent link to this record
 

 
Author Bogdan Raducanu; Jordi Vitria; Ales Leonardis edit  url
doi  openurl
  Title Online pattern recognition and machine learning techniques for computer-vision: Theory and applications Type Journal Article
  Year 2010 Publication Image and Vision Computing Abbreviated Journal (down) IMAVIS  
  Volume 28 Issue 7 Pages 1063–1064  
  Keywords  
  Abstract (Editorial for the Special Issue on Online pattern recognition and machine learning techniques)
In real life, visual learning is supposed to be a continuous process. This paradigm has found its way also in artificial vision systems. There is an increasing trend in pattern recognition represented by online learning approaches, which aims at continuously updating the data representation when new information arrives. Starting with a minimal dataset, the initial knowledge is expanded by incorporating incoming instances, which may have not been previously available or foreseen at the system’s design stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest in this subject, the aim of this special issue is to be a landmark event in the development of online learning techniques and their applications with the hope that it will capture the interest of a wider audience and will attract even more researchers. We received 19 contributions, of which 9 have been accepted for publication, after having been subjected to usual peer review process.
 
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RVL2010 Serial 1280  
Permanent link to this record
 

 
Author Bogdan Raducanu; Jordi Vitria edit  openurl
  Title Face Recognition by Artificial Vision Systems: A Cognitive Perspective Type Journal
  Year 2008 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal (down) IJPRAI  
  Volume 22 Issue 5 Pages 899–913  
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  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RaV2008b Serial 1007  
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