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    Gaze estimation problem tackled through synthetic images

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    2020101204_Garde_GazeEstimation.pdf (229.7Kb)
    Date
    2020
    Author
    Garde Lecumberri, Gonzalo Upna
    Larumbe Bergera, Andoni Upna Orcid
    Bossavit, Benoît Upna Orcid
    Cabeza Laguna, Rafael Upna Orcid
    Porta Cuéllar, Sonia Upna Orcid
    Villanueva Larre, Arantxa Upna Orcid
    Version
    Acceso abierto / Sarbide irekia
    Type
    Contribución a congreso / Biltzarrerako ekarpena
    Version
    Versión aceptada / Onetsi den bertsioa
    Project Identifier
    ES/1PE/TIN2016-84388-R 
    Impact
     
     
     
    10.1145/3379156.3391368
     
     
     
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    Abstract
    In this paper, we evaluate a synthetic framework to be used in the field of gaze estimation employing deep learning techniques. The lack of sufficient annotated data could be overcome by the utilization of a synthetic evaluation framework as far as it resembles the behavior of a real scenario. In this work, we use U2Eyes synthetic environment employing I2Head datataset as real benchmark for compa ... [++]
    In this paper, we evaluate a synthetic framework to be used in the field of gaze estimation employing deep learning techniques. The lack of sufficient annotated data could be overcome by the utilization of a synthetic evaluation framework as far as it resembles the behavior of a real scenario. In this work, we use U2Eyes synthetic environment employing I2Head datataset as real benchmark for comparison based on alternative training and testing strategies. The results obtained show comparable average behavior between both frameworks although significantly more robust and stable performance is retrieved by the synthetic images. Additionally, the potential of synthetically pretrained models in order to be applied in user's specific calibration strategies is shown with outstanding performances. [--]
    Subject
    Neural networks, Datasets gaze estimation
     
    Publisher
    Association for Computing Machinery (ACM)
    Published in
    ETRA'20 Short Papers: ACM Symposium on Eye Tracking Research and Applications, 2020:16
    Description
    Trabajo presentado al Symposium on Eye Tracking Research and Applications (ETRA ’20 Short Papers). Stuttgart, 2020
    Departament
    Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación / Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektriko, Elektroniko eta Telekomunikazio Saila
     
    Publisher version
    https://doi.org/10.1145/3379156.3391368
    URI
    https://hdl.handle.net/2454/39351
    Sponsorship
    The authors would like to acknowledge the Spanish Ministry of Science, Innovation and Universities for their support under Contract TIN2017-84388-R.
    Appears in Collections
    • Comunicaciones y ponencias de congresos DIEC - IEKS Biltzarretako komunikazioak eta txostenak [89]
    • Comunicaciones y ponencias de congresos - Biltzarrak eta Argitalpenak [571]
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