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Author | Javier M. Olaso; Alain Vazquez; Leila Ben Letaifa; Mikel de Velasco; Aymen Mtibaa; Mohamed Amine Hmani; Dijana Petrovska-Delacretaz; Gerard Chollet; Cesar Montenegro; Asier Lopez-Zorrilla; Raquel Justo; Roberto Santana; Jofre Tenorio-Laranga; Eduardo Gonzalez-Fraile; Begoña Fernandez-Ruanova; Gennaro Cordasco; Anna Esposito; Kristin Beck Gjellesvik; Anna Torp Johansen; Maria Stylianou Kornes; Colin Pickard; Cornelius Glackin; Gary Cahalane; Pau Buch; Cristina Palmero; Sergio Escalera; Olga Gordeeva; Olivier Deroo; Anaïs Fernandez; Daria Kyslitska; Jose Antonio Lozano; Maria Ines Torres; Stephan Schlogl | ||||
Title | The EMPATHIC Virtual Coach: a demo | Type | Conference Article | ||
Year | 2021 | Publication | 23rd ACM International Conference on Multimodal Interaction | Abbreviated Journal | |
Volume | Issue | Pages | 848-851 | ||
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Abstract | The main objective of the EMPATHIC project has been the design and development of a virtual coach to engage the healthy-senior user and to enhance well-being through awareness of personal status. The EMPATHIC approach addresses this objective through multimodal interactions supported by the GROW coaching model. The paper summarizes the main components of the EMPATHIC Virtual Coach (EMPATHIC-VC) and introduces a demonstration of the coaching sessions in selected scenarios. | ||||
Address | Virtual; October 2021 | ||||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICMI | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ OVB2021 | Serial | 3644 | ||
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Author | Reza Azad; Afshin Bozorgpour; Maryam Asadi-Aghbolaghi; Dorit Merhof; Sergio Escalera | ||||
Title | Deep Frequency Re-Calibration U-Net for Medical Image Segmentation | Type | Conference Article | ||
Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 3274-3283 | ||
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Abstract | We present a novel solution to the garment animation problem through deep learning. Our contribution allows animating any template outfit with arbitrary topology and geometric complexity. Recent works develop models for garment edition, resizing and animation at the same time by leveraging the support body model (encoding garments as body homotopies). This leads to complex engineering solutions that suffer from scalability, applicability and compatibility. By limiting our scope to garment animation only, we are able to propose a simple model that can animate any outfit, independently of its topology, vertex order or connectivity. Our proposed architecture maps outfits to animated 3D models into the standard format for 3D animation (blend weights and blend shapes matrices), automatically providing of compatibility with any graphics engine. We also propose a methodology to complement supervised learning with an unsupervised physically based learning that implicitly solves collisions and enhances cloth quality. | ||||
Address | VIRTUAL; October 2021 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ ABA2021 | Serial | 3645 | ||
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Author | Ajian Liu; Chenxu Zhao; Zitong Yu; Anyang Su; Xing Liu; Zijian Kong; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zhen Lei; Guodong Guo | ||||
Title | 3D High-Fidelity Mask Face Presentation Attack Detection Challenge | Type | Conference Article | ||
Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 814-823 | ||
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Abstract | The threat of 3D mask to face recognition systems is increasing serious, and has been widely concerned by researchers. To facilitate the study of the algorithms, a large-scale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask) has been collected. Specifically, it consists of total amount of 54,600 videos which are recorded from 75 subjects with 225 realistic masks under 7 new kinds of sensors. Based on this dataset and Protocol 3 which evaluates both the discrimination and generalization ability of the algorithm under the open set scenarios, we organized a 3D High-Fidelity Mask Face Presentation Attack Detection Challenge to boost the research of 3D mask based attack detection. It attracted more than 200 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-ran by the organizing team, and the results were used for the final ranking. This paper presents an overview of the challenge, including the introduction of the dataset used, the definition of the protocol, the calculation of the evaluation criteria, and the summary and publication of the competition results. Finally, we focus on introducing and analyzing the top ranked algorithms, the conclusion summary, and the research ideas for mask attack detection provided by this competition. | ||||
Address | Virtual; October 2021 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ LZY2021 | Serial | 3646 | ||
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Author | Claudia Greco; Carmela Buono; Pau Buch-Cardona; Gennaro Cordasco; Sergio Escalera; Anna Esposito; Anais Fernandez; Daria Kyslitska; Maria Stylianou Kornes; Cristina Palmero; Jofre Tenorio Laranga; Anna Torp Johansen; Maria Ines Torres | ||||
Title | Emotional Features of Interactions With Empathic Agents | Type | Conference Article | ||
Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 2168-2176 | ||
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Abstract | The current study is part of the EMPATHIC project, whose aim is to develop an Empathic Virtual Coach (VC) capable of promoting healthy and independent aging. To this end, the VC needs to be capable of perceiving the emotional states of users and adjusting its behaviour during the interactions according to what the users are experiencing in terms of emotions and comfort. Thus, the present work focuses on some sessions where elderly users of three different countries interact with a simulated system. Audio and video information extracted from these sessions were examined by external observers to assess participants' emotional experience with the EMPATHIC-VC in terms of categorical and dimensional assessment of emotions. Analyses were conducted on the emotional labels assigned by the external observers while participants were engaged in two different scenarios: a generic one, where the interaction was carried out with no intention to discuss a specific topic, and a nutrition one, aimed to accomplish a conversation on users' nutritional habits. Results of analyses performed on both audio and video data revealed that the EMPATHIC coach did not elicit negative feelings in the users. Indeed, users from all countries have shown relaxed and positive behavior when interacting with the simulated VC during both scenarios. Overall, the EMPATHIC-VC was capable to offer an enjoyable experience without eliciting negative feelings in the users. This supports the hypothesis that an Empathic Virtual Coach capable of considering users' expectations and emotional states could support elderly people in daily life activities and help them to remain independent. | ||||
Address | VIRTUAL; October 2021 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ GBB2021 | Serial | 3647 | ||
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Author | David Curto; Albert Clapes; Javier Selva; Sorina Smeureanu; Julio C. S. Jacques Junior; David Gallardo-Pujol; Georgina Guilera; David Leiva; Thomas B. Moeslund; Sergio Escalera; Cristina Palmero | ||||
Title | Dyadformer: A Multi-Modal Transformer for Long-Range Modeling of Dyadic Interactions | Type | Conference Article | ||
Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 2177-2188 | ||
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Abstract | Personality computing has become an emerging topic in computer vision, due to the wide range of applications it can be used for. However, most works on the topic have focused on analyzing the individual, even when applied to interaction scenarios, and for short periods of time. To address these limitations, we present the Dyadformer, a novel multi-modal multi-subject Transformer architecture to model individual and interpersonal features in dyadic interactions using variable time windows, thus allowing the capture of long-term interdependencies. Our proposed cross-subject layer allows the network to explicitly model interactions among subjects through attentional operations. This proof-of-concept approach shows how multi-modality and joint modeling of both interactants for longer periods of time helps to predict individual attributes. With Dyadformer, we improve state-of-the-art self-reported personality inference results on individual subjects on the UDIVA v0.5 dataset. | ||||
Address | Virtual; October 2021 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CCS2021 | Serial | 3648 | ||
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Author | Neelu Madan; Arya Farkhondeh; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund | ||||
Title | Temporal Cues From Socially Unacceptable Trajectories for Anomaly Detection | Type | Conference Article | ||
Year | 2021 | Publication | IEEE/CVF International Conference on Computer Vision Workshops | Abbreviated Journal | |
Volume | Issue | Pages | 2150-2158 | ||
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Abstract | State-of-the-Art (SoTA) deep learning-based approaches to detect anomalies in surveillance videos utilize limited temporal information, including basic information from motion, e.g., optical flow computed between consecutive frames. In this paper, we compliment the SoTA methods by including long-range dependencies from trajectories for anomaly detection. To achieve that, we first created trajectories by running a tracker on two SoTA datasets, namely Avenue and Shanghai-Tech. We propose a prediction-based anomaly detection method using trajectories based on Social GANs, also called in this paper as temporal-based anomaly detection. Then, we hypothesize that late fusion of the result of this temporal-based anomaly detection system with spatial-based anomaly detection systems produces SoTA results. We verify this hypothesis on two spatial-based anomaly detection systems. We show that both cases produce results better than baseline spatial-based systems, indicating the usefulness of the temporal information coming from the trajectories for anomaly detection. We observe that the proposed approach depicts the maximum improvement in micro-level Area-Under-the-Curve (AUC) by 4.1% on CUHK Avenue and 3.4% on Shanghai-Tech over one of the baseline method. We also show a high performance on cross-data evaluation, where we learn the weights to combine spatial and temporal information on Shanghai-Tech and perform evaluation on CUHK Avenue and vice-versa. | ||||
Address | Virtual; October 2021 | ||||
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Area | Expedition | Conference | ICCVW | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MFN2021 | Serial | 3649 | ||
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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 | ||||
Title | DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification | Type | Journal Article | ||
Year | 2021 | Publication | BMC Bioinformatics | Abbreviated Journal | |
Volume | 22 | Issue | Pages | 473 | |
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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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Author | Javier Marin; Sergio Escalera | ||||
Title | SSSGAN: Satellite Style and Structure Generative Adversarial Networks | Type | Journal Article | ||
Year | 2021 | Publication | Remote Sensing | Abbreviated Journal | |
Volume | 13 | Issue | 19 | Pages | 3984 |
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Abstract | This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure, in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce
consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows us to control the generation not only with respect to the desired structure, but also with respect to a geographic area. |
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MaE2021 | Serial | 3651 | ||
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Author | Victor M. Campello; Polyxeni Gkontra; Cristian Izquierdo; Carlos Martin-Isla; Alireza Sojoudi; Peter M. Full; Klaus Maier-Hein; Yao Zhang; Zhiqiang He; Jun Ma; Mario Parreno; Alberto Albiol; Fanwei Kong; Shawn C. Shadden; Jorge Corral Acero; Vaanathi Sundaresan; Mina Saber; Mustafa Elattar; Hongwei Li; Bjoern Menze; Firas Khader; Christoph Haarburger; Cian M. Scannell; Mitko Veta; Adam Carscadden; Kumaradevan Punithakumar; Xiao Liu; Sotirios A. Tsaftaris; Xiaoqiong Huang; Xin Yang; Lei Li; Xiahai Zhuang; David Vilades; Martin L. Descalzo; Andrea Guala; Lucia La Mura; Matthias G. Friedrich; Ria Garg; Julie Lebel; Filipe Henriques; Mahir Karakas; Ersin Cavus; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Jose F. Rodriguez Palomares; Karim Lekadir | ||||
Title | Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal | TMI |
Volume | 40 | Issue | 12 | Pages | 3543-3554 |
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Abstract | The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CGI2021 | Serial | 3653 | ||
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Author | Meysam Madadi; Hugo Bertiche; Sergio Escalera | ||||
Title | Deep unsupervised 3D human body reconstruction from a sparse set of landmarks | Type | Journal Article | ||
Year | 2021 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 129 | Issue | Pages | 2499–2512 | |
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Abstract | In this paper we propose the first deep unsupervised approach in human body reconstruction to estimate body surface from a sparse set of landmarks, so called DeepMurf. We apply a denoising autoencoder to estimate missing landmarks. Then we apply an attention model to estimate body joints from landmarks. Finally, a cascading network is applied to regress parameters of a statistical generative model that reconstructs body. Our set of proposed loss functions allows us to train the network in an unsupervised way. Results on four public datasets show that our approach accurately reconstructs the human body from real world mocap data. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MBE2021 | Serial | 3654 | ||
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Author | Meysam Madadi; Hugo Bertiche; Wafa Bouzouita; Isabelle Guyon; Sergio Escalera | ||||
Title | Learning Cloth Dynamics: 3D+Texture Garment Reconstruction Benchmark | Type | Conference Article | ||
Year | 2021 | Publication | Proceedings of Machine Learning Research | Abbreviated Journal | |
Volume | 133 | Issue | Pages | 57-76 | |
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Abstract | Human avatars are important targets in many computer applications. Accurately tracking, capturing, reconstructing and animating the human body, face and garments in 3D are critical for human-computer interaction, gaming, special effects and virtual reality. In the past, this has required extensive manual animation. Regardless of the advances in human body and face reconstruction, still modeling, learning and analyzing human dynamics need further attention. In this paper we plan to push the research in this direction, e.g. understanding human dynamics in 2D and 3D, with special attention to garments. We provide a large-scale dataset (more than 2M frames) of animated garments with variable topology and type, calledCLOTH3D++. The dataset contains RGBA video sequences paired with its corresponding 3D data. We pay special care to garment dynamics and realistic rendering of RGB data, including lighting, fabric type and texture. With this dataset, we hold a competition at NeurIPS2020. We design three tracks so participants can compete to develop the best method to perform 3D garment reconstruction in a sequence from (1) 3D-to-3D garments, (2) RGB-to-3D garments, and (3) RGB-to-3D garments plus texture. We also provide a baseline method, based on graph convolutional networks, for each track. Baseline results show that there is a lot of room for improvements. However, due to the challenging nature of the problem, no participant could outperform the baselines. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ MBB2021 | Serial | 3655 | ||
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Author | Swathikiran Sudhakaran; Sergio Escalera;Oswald Lanz | ||||
Title | Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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Abstract | We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets. The core component of EgoACO is class activation pooling (CAP), a differentiable pooling operation that combines ideas from bilinear pooling for fine-grained recognition and from feature learning for discriminative localization. CAP uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions. Through CAP, EgoACO learns to decode object and scene context descriptors from video frame features. For temporal modeling in EgoACO, we design a recurrent version of class activation pooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional gated LSTM with built-in spatial attention and a re-designed output gate. Action, object and context descriptors are fused by a multi-head prediction that accounts for the inter-dependencies between noun-verb-action structured labels in egocentric video datasets. EgoACO features built-in visual explanations, helping learning and interpretation. Results on the two largest egocentric action recognition datasets currently available, EPIC-KITCHENS and EGTEA, show that by explicitly decoding action-context-object descriptors, EgoACO achieves state-of-the-art recognition performance. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ SEL2021 | Serial | 3656 | ||
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Author | Fatemeh Noroozi; Ciprian Corneanu; Dorota Kamińska; Tomasz Sapiński; Sergio Escalera; Gholamreza Anbarjafari | ||||
Title | Survey on Emotional Body Gesture Recognition | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 12 | Issue | 2 | Pages | 505 - 523 |
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Abstract | Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as “body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g. human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce, there is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ NCK2021 | Serial | 3657 | ||
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Author | Kaustubh Kulkarni; Ciprian Corneanu; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarjafari | ||||
Title | Automatic Recognition of Facial Displays of Unfelt Emotions | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
Volume | 12 | Issue | 2 | Pages | 377 - 390 |
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Abstract | Humans modify their facial expressions in order to communicate their internal states and sometimes to mislead observers regarding their true emotional states. Evidence in experimental psychology shows that discriminative facial responses are short and subtle. This suggests that such behavior would be easier to distinguish when captured in high resolution at an increased frame rate. We are proposing SASE-FE, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states. We show that overall the problem of recognizing whether facial movements are expressions of authentic emotions or not can be successfully addressed by learning spatio-temporal representations of the data. For this purpose, we propose a method that aggregates features along fiducial trajectories in a deeply learnt space. Performance of the proposed model shows that on average, it is easier to distinguish among genuine facial expressions of emotion than among unfelt facial expressions of emotion and that certain emotion pairs such as contempt and disgust are more difficult to distinguish than the rest. Furthermore, the proposed methodology improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ KCO2021 | Serial | 3658 | ||
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Author | Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus | ||||
Title | Mobile eHealth Platform for Home Monitoring of Bipolar Disorder | Type | Conference Article | ||
Year | 2021 | Publication | 27th ACM International Conference on Multimedia Modeling | Abbreviated Journal | |
Volume | 12573 | Issue | Pages | 330-341 | |
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Abstract | People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur.
MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians. |
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Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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Area | Expedition | Conference | MMM | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CEE2021 | Serial | 3659 | ||
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