Martinikorena Aranburu, Ion

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https://academica-e.unavarra.es/handle/2454/49544

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Martinikorena Aranburu

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Ion

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Ingeniería Eléctrica y Electrónica

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0000-0002-5035-0274

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751437

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810993

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2018 - 20203
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Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen Access
    Low cost gaze estimation: knowledge-based solutions
    (IEEE, 2020) Martinikorena Aranburu, Ion; Larumbe Bergera, Andoni; Ariz Galilea, Mikel; Porta Cuéllar, Sonia; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user’s displacement. Accuracy values of about 3◦ have been obtained, increasing to values close to 5◦ in extreme displacement settings, results fully comparable with the state-of-the-art.
  • Thumbnail Image
    PublicationOpen Access
    Fast and robust ellipse detection algorithm for head-mounted eye tracking systems
    (Springer, 2018) Martinikorena Aranburu, Ion; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Urtasun, Iñaki; Larumbe Bergera, Andoni; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior.
  • Thumbnail Image
    PublicationOpen Access
    Introducing I2Head database
    (ACM (Association for Computing Machinery), 2018) Martinikorena Aranburu, Ion; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Porta Cuéllar, Sonia; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    I2Head database has been created with the aim to become an optimal reference for low cost gaze estimation. It exhibits the following outstanding characteristics: it takes into account key aspects of low resolution eye tracking technology; it combines images of users gazing at different grids of points from alternative positions with registers of user's head position and it provides calibration information of the camera and a simple 3D head model for each user. Hardware used to build the database includes a 6D magnetic sensor and a webcam. A careful calibration method between the sensor and the camera has been developed to guarantee the accuracy of the data. Different sessions have been recorded for each user including not only static head scenarios but also controlled displacements and even free head movements. The database is an outstanding framework to test both gaze estimation algorithms and head pose estimation methods.