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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guçlu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia Liem; Marcel A. J. Van Gerven; Rob Van Lier edit   pdf
url  doi
openurl 
  Title Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Journal Article
  Year (down) 2022 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 13 Issue 2 Pages 894-911  
  Keywords  
  Abstract Explainability and interpretability are two critical aspects of decision support systems. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of apparent personality recognition. To the best of our knowledge, this is the first effort in this direction. We describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, evaluation protocol, proposed solutions and summarize the results of the challenge. We investigate the issue of bias in detail. Finally, derived from our study, we outline research opportunities that we foresee will be relevant in this area in the near future.  
  Address 1 April-June 2022  
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  Notes HuPBA; no menciona Approved no  
  Call Number Admin @ si @ EKS2020 Serial 3406  
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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 (down) 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 HuPBA Approved no  
  Call Number Admin @ si @ Serial 3724  
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Author Jun Wan; Chi Lin; Longyin Wen; Yunan Li; Qiguang Miao; Sergio Escalera; Gholamreza Anbarjafari; Isabelle Guyon; Guodong Guo; Stan Z. Li edit   pdf
url  doi
openurl 
  Title ChaLearn Looking at People: IsoGD and ConGD Large-scale RGB-D Gesture Recognition Type Journal Article
  Year (down) 2022 Publication IEEE Transactions on Cybernetics Abbreviated Journal TCIBERN  
  Volume 52 Issue 5 Pages 3422-3433  
  Keywords  
  Abstract The ChaLearn large-scale gesture recognition challenge has been run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams round the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. This paper describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. We discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition, and provide a detailed analysis of the current state-of-the-art methods for large-scale isolated and continuous gesture recognition based on RGB-D video sequences. In addition to recognition rate and mean jaccard index (MJI) as evaluation metrics used in our previous challenges, we also introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) baseline method, determining the video division points based on the skeleton points extracted by convolutional pose machine (CPM). Experiments demonstrate that the proposed Bi-LSTM outperforms the state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.  
  Address May 2022  
  Corporate Author Thesis  
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ WLW2020 Serial 3522  
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera edit  url
doi  openurl
  Title Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD Type Journal Article
  Year (down) 2022 Publication Journal of Ambient Intelligence and Humanized Computing Abbreviated Journal  
  Volume 13 Issue Pages 591–611  
  Keywords  
  Abstract One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ RKE2021c Serial 3660  
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Author Clementine Decamps; Alexis Arnaud; Florent Petitprez; Mira Ayadi; Aurelia Baures; Lucile Armenoult; Sergio Escalera; Isabelle Guyon; Remy Nicolle; Richard Tomasini; Aurelien de Reynies; Jerome Cros; Yuna Blum; Magali Richard edit   pdf
url  openurl
  Title DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification Type Journal Article
  Year (down) 2021 Publication BMC Bioinformatics Abbreviated Journal  
  Volume 22 Issue Pages 473  
  Keywords  
  Abstract Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ DAP2021 Serial 3650  
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