TECNICAS DE APRENDIZAJE AUTOMATICO PARA SISTEMAS DE SEGUIMIENTO DE MIRADA
TIN2017-84388-R
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Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de I+D+i Orientada a los Retos de la Sociedad
Subprograma Programa Estatal de I+D+i Orientada a los Retos de la Sociedad
Convocatoria Retos Investigación: Proyectos I+D+i
Año convocatoria 2017
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016
Centro beneficiario UNIVERSIDAD PUBLICA DE NAVARRA
Identificador persistente http://dx.doi.org/10.13039/501100011033
Publicaciones
Resultados totales (Incluyendo duplicados): 5
Encontrada(s) 1 página(s)
Encontrada(s) 1 página(s)
SeTA: semiautomatic tool for annotation of eye tracking images
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Larumbe Bergera, Andoni
- Porta Cuéllar, Sonia
- Cabeza Laguna, Rafael
- Villanueva Larre, Arantxa
Availability of large scale tagged datasets is a must in the field of deep learning applied to the eye tracking challenge. In this paper, the potential of Supervised-Descent-Method (SDM) as a semiautomatic labelling tool for eye tracking images is shown. The objective of the paper is to evidence how the human effort needed for manually labelling large eye tracking datasets can be radically reduced by the use of cascaded regressors. Different applications are provided in the fields of high and low resolution systems. An iris/pupil center labelling is shown as example for low resolution images while a pupil contour points detection is demonstrated in high resolution. In both cases manual annotation requirements are drastically reduced., Spanish Ministry of Science, Innovation and Universities, contract TIN2017-84388-R
Proyecto: AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84388-R
Low cost gaze estimation: knowledge-based solutions
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Martinikorena Aranburu, Ion
- Larumbe Bergera, Andoni
- Ariz Galilea, Mikel
- Porta Cuéllar, Sonia
- Cabeza Laguna, Rafael
- Villanueva Larre, Arantxa
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., This work was supported in part by the Ministry of Economy and Competitiveness under Grant TIN2014-52897-R and in part by the Ministry of Science, Innovation and Universities under Grant TIN2017-84388-R.
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., This work was supported in part by the Ministry of Economy and Competitiveness under Grant TIN2014-52897-R and in part by the Ministry of Science, Innovation and Universities under Grant TIN2017-84388-R.
Introducing I2Head database
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Martinikorena Aranburu, Ion
- Cabeza Laguna, Rafael
- Villanueva Larre, Arantxa
- Porta Cuéllar, Sonia
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., The authors would like to acknowledge the Spanish Ministry of Economy, Industry and Competitiveness for their support under Contracts TIN2014-52897-R and TIN2017-84388-R in the framework of the National Plan of I+D+i.
Supervised descent method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Larumbe Bergera, Andoni
- Cabeza Laguna, Rafael
- Villanueva Larre, Arantxa
The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, Supervised Descent Method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking., Spanish Ministry of Economy,Industry and Competitiveness, contracts TIN2014-52897-R and TIN2017-84388-R
U2Eyes: a binocular dataset for eye tracking and gaze estimation
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Porta Cuéllar, Sonia
- Bossavit, Benoît
- Cabeza Laguna, Rafael
- Larumbe Bergera, Andoni
- Garde Lecumberri, Gonzalo
- Villanueva Larre, Arantxa
Theory shows that huge amount of labelled data are needed in order to achieve reliable classification/regression methods when using deep/machine learning techniques. However, in the eye tracking field, manual annotation is not a feasible option due to the wide variability to be covered. Hence, techniques devoted to synthesizing images show up as an opportunity to provide vast amounts of annotated data. Considering that the well-known UnityEyes tool provides a framework to generate single eye images and taking into account that both eyes information can contribute to improve gaze estimation accuracy we present U2Eyes dataset, that is publicly available. It comprehends about 6 million of synthetic images containing binocular data. Furthermore, the physiology of the eye model employed is improved, simplified dynamics of binocular vision are incorporated and more detailed 2D and 3D labelled data are provided. Additionally, an example of application of the dataset is shown as work in progress. Employing U2Eyes as training framework Supervised Descent Method (SDM) is used for eyelids segmentation. The model obtained as result of the training process is then applied on real images from GI4E dataset showing promising results., The authors would like to acknowledge the Spanish Ministry of Science, Innovation and Universities for their support under Contract TIN2017-84388-R.
Proyecto: AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84388-R