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Author Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera edit   pdf
doi  openurl
  Title A Gesture Recognition System for Detecting Behavioral Patterns of ADHD Type Journal Article
  Year (down) 2016 Publication IEEE Transactions on System, Man and Cybernetics, Part B Abbreviated Journal TSMCB  
  Volume 46 Issue 1 Pages 136-147  
  Keywords Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data  
  Abstract We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.  
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  Area Expedition Conference  
  Notes HuPBA; MILAB; Approved no  
  Call Number Admin @ si @ BHE2016 Serial 2566  
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Author Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera edit   pdf
doi  openurl
  Title Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History Type Journal Article
  Year (down) 2016 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 28 Issue 8 Pages 1548-1568  
  Keywords Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal  
  Abstract Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.  
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  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ COC2016 Serial 2718  
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Author Antonio Hernandez; Sergio Escalera; Stan Sclaroff edit  doi
openurl 
  Title Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures Type Journal Article
  Year (down) 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 118 Issue 1 Pages 49–64  
  Keywords Contextual rescoring; Poselets; Human pose estimation  
  Abstract In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly.  
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  Publisher Springer US Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ HES2016 Serial 2719  
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Author Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria edit   pdf
doi  openurl
  Title Automatic garment retexturing based on infrared information Type Journal Article
  Year (down) 2016 Publication Computers & Graphics Abbreviated Journal CG  
  Volume 59 Issue Pages 28-38  
  Keywords Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading  
  Abstract This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms.  
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  Publisher Elsevier Place of Publication Editor  
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  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ ADT2016 Serial 2759  
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Author Sergio Escalera; Vassilis Athitsos; Isabelle Guyon edit  url
openurl 
  Title Challenges in multimodal gesture recognition Type Journal Article
  Year (down) 2016 Publication Journal of Machine Learning Research Abbreviated Journal JMLR  
  Volume 17 Issue Pages 1-54  
  Keywords Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM  
  Abstract This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor Zhuowen Tu  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA;MILAB; Approved no  
  Call Number Admin @ si @ EAG2016 Serial 2764  
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