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Author Julio C. S. Jacques Junior; Yagmur Gucluturk; Marc Perez; Umut Guçlu; Carlos Andujar; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Marcel A. J. van Gerven; Rob van Lier; Sergio Escalera edit  doi
openurl 
  Title First Impressions: A Survey on Vision-Based Apparent Personality Trait Analysis Type Journal Article
  Year 2022 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 13 Issue 1 Pages 75-95  
  Keywords Personality computing; first impressions; person perception; big-five; subjective bias; computer vision; machine learning; nonverbal signals; facial expression; gesture; speech analysis; multi-modal recognition  
  Abstract Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.  
  Address 1 Jan.-March 2022  
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  Notes (up) HuPBA Approved no  
  Call Number Admin @ si @ Serial 3724  
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Author Penny Tarling; Mauricio Cantor; Albert Clapes; Sergio Escalera edit  doi
openurl 
  Title Deep learning with self-supervision and uncertainty regularization to count fish in underwater images Type Journal Article
  Year 2022 Publication PloS One Abbreviated Journal Plos  
  Volume 17 Issue 5 Pages e0267759  
  Keywords  
  Abstract Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images. We introduce a large dataset of sonar videos, deployed to record wild Lebranche mullet schools (Mugil liza), with a subset of 500 labelled images. We utilise abundant unlabelled data in a self-supervised task to improve the supervised counting task. For the first time in this context, by introducing uncertainty quantification, we improve model training and provide an accompanying measure of prediction uncertainty for more informed biological decision-making. Finally, we demonstrate the generalisability of our proposed counting framework through testing it on a recent benchmark dataset of high-resolution annotated underwater images from varying habitats (DeepFish). From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task. By providing an open-source framework along with training data, our study puts forth an efficient deep learning template for crowd counting aquatic animals thereby contributing effective methods to assess natural populations from the ever-increasing visual data.  
  Address  
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  Publisher Public Library of Science Place of Publication Editor  
  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes (up) HuPBA Approved no  
  Call Number Admin @ si @ Serial 3743  
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Author Raquel Justo; Leila Ben Letaifa; Cristina Palmero; Eduardo Gonzalez-Fraile; Anna Torp Johansen; Alain Vazquez; Gennaro Cordasco; Stephan Schlogl; Begoña Fernandez-Ruanova; Micaela Silva; Sergio Escalera; Mikel de Velasco; Joffre Tenorio-Laranga; Anna Esposito; Maria Korsnes; M. Ines Torres edit  url
openurl 
  Title Analysis of the Interaction between Elderly People and a Simulated Virtual Coach, Journal of Ambient Intelligence and Humanized Computing Type Journal Article
  Year 2020 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal AIHC  
  Volume 11 Issue 12 Pages 6125-6140  
  Keywords  
  Abstract The EMPATHIC project develops and validates new interaction paradigms for personalized virtual coaches (VC) to promote healthy and independent aging. To this end, the work presented in this paper is aimed to analyze the interaction between the EMPATHIC-VC and the users. One of the goals of the project is to ensure an end-user driven design, involving senior users from the beginning and during each phase of the project. Thus, the paper focuses on some sessions where the seniors carried out interactions with a Wizard of Oz driven, simulated system. A coaching strategy based on the GROW model was used throughout these sessions so as to guide interactions and engage the elderly with the goals of the project. In this interaction framework, both the human and the system behavior were analyzed. The way the wizard implements the GROW coaching strategy is a key aspect of the system behavior during the interaction. The language used by the virtual agent as well as his or her physical aspect are also important cues that were analyzed. Regarding the user behavior, the vocal communication provides information about the speaker’s emotional status, that is closely related to human behavior and which can be extracted from the speech and language analysis. In the same way, the analysis of the facial expression, gazes and gestures can provide information on the non verbal human communication even when the user is not talking. In addition, in order to engage senior users, their preferences and likes had to be considered. To this end, the effect of the VC on the users was gathered by means of direct questionnaires. These analyses have shown a positive and calm behavior of users when interacting with the simulated virtual coach as well as some difficulties of the system to develop the proposed coaching strategy.  
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  Area Expedition Conference  
  Notes (up) HuPBA; no proj Approved no  
  Call Number Admin @ si @ JLP2020 Serial 3443  
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Author Albert Clapes; Miguel Reyes; Sergio Escalera edit   pdf
url  doi
openurl 
  Title Multi-modal User Identification and Object Recognition Surveillance System Type Journal Article
  Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 34 Issue 7 Pages 799-808  
  Keywords Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning  
  Abstract We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes (up) HUPBA; 600.046; 605.203;MILAB Approved no  
  Call Number Admin @ si @ CRE2013 Serial 2248  
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Author Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez edit   pdf
url  doi
openurl 
  Title Top-down model fitting for hand pose recovery in sequences of depth images Type Journal Article
  Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 79 Issue Pages 63-75  
  Keywords  
  Abstract State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.  
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  Area Expedition Conference  
  Notes (up) HUPBA; 600.098 Approved no  
  Call Number Admin @ si @ MEC2018 Serial 3203  
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