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Kaustubh Kulkarni; Ciprian Corneanu; Ikechukwu Ofodile; Sergio Escalera; Xavier Baro; Sylwia Hyniewska; Juri Allik; Gholamreza Anbarjafari |
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Automatic Recognition of Facial Displays of Unfelt Emotions |
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2021 |
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IEEE Transactions on Affective Computing |
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TAC |
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12 |
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2 |
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377 - 390 |
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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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HUPBA; no proj |
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Admin @ si @ KCO2021 |
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3658 |
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Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats |
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Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription |
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2017 |
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Journal of Medical Engineering & Technology |
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JMET |
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41 |
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6 |
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486-497 |
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INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question.
OBJECTIVES:
To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination.
METHODS:
Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows.
RESULTS:
A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation).
CONCLUSIONS:
Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs. |
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HUPBA; no menciona |
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Admin @ si @ EPB2017 |
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3010 |
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Alvaro Cepero; Albert Clapes; Sergio Escalera |
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Automatic non-verbal communication skills analysis: a quantitative evaluation |
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2015 |
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AI Communications |
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AIC |
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28 |
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1 |
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87-101 |
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Social signal processing; human behavior analysis; multi-modal data description; multi-modal data fusion; non-verbal communication analysis; e-Learning |
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The oral communication competence is defined on the top of the most relevant skills for one's professional and personal life. Because of the importance of communication in our activities of daily living, it is crucial to study methods to evaluate and provide the necessary feedback that can be used in order to improve these communication capabilities and, therefore, learn how to express ourselves better. In this work, we propose a system capable of evaluating quantitatively the quality of oral presentations in an automatic fashion. The system is based on a multi-modal RGB, depth, and audio data description and a fusion approach in order to recognize behavioral cues and train classifiers able to eventually predict communication quality levels. The performance of the proposed system is tested on a novel dataset containing Bachelor thesis' real defenses, presentations from an 8th semester Bachelor courses, and Master courses' presentations at Universitat de Barcelona. Using as groundtruth the marks assigned by actual instructors, our system achieves high performance categorizing and ranking presentations by their quality, and also making real-valued mark predictions. |
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0921-7126 |
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HUPBA;MILAB |
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Admin @ si @ CCE2015 |
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2549 |
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Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria |
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Automatic garment retexturing based on infrared information |
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2016 |
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Computers & Graphics |
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CG |
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59 |
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28-38 |
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Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading |
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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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Elsevier |
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HuPBA;MILAB; |
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Admin @ si @ ADT2016 |
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2759 |
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Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera |
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Automatic Digital Biometry Analysis based on Depth Maps |
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Journal Article |
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2013 |
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Computers in Industry |
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COMPUTIND |
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64 |
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9 |
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1316-1325 |
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Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis |
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World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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Elsevier |
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HuPBA;MILAB |
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Admin @ si @ RCR2013 |
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2252 |
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