Bengoechea Irañeta, José Javier

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https://academica-e.unavarra.es/handle/2454/48701

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Bengoechea Irañeta

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José Javier

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Ingeniería Eléctrica y Electrónica

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1173991

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810426

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2014 - 20182
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Author: Villanueva Larre, Arantxa × Has files: true ×

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    PublicationOpen Access
    Evaluation of accurate eye corner detection methods for gaze estimation
    (Bern Open Publishing, 2014) Bengoechea Irañeta, José Javier; Cerrolaza Martínez, Juan José; Villanueva Larre, Arantxa; Cabeza Laguna, Rafael; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface.
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    PublicationOpen Access
    Improved strategies for HPE employing learning-by-synthesis approaches
    (IEEE, 2018) Larumbe Bergera, Andoni; Ariz Galilea, Mikel; Bengoechea Irañeta, José Javier; Segura, Rubén; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    The first contribution of this paper is the presentation of a synthetic video database where the groundtruth of 2D facial landmarks and 3D head poses is available to be used for training and evaluating Head Pose Estimation (HPE) methods. The database is publicly available and contains videos of users performing guided and natural movements. The second and main contribution is the submission of a hybrid method for HPE based on Pose from Ortography and Scaling by Iterations (POSIT). The 2D landmark detection is performed using Random Cascaded-Regression Copse (R-CR-C). For the training stage we use, state of the art labeled databases. Learning-by-synthesis approach has been also used to augment the size of the database employing the synthetic database. HPE accuracy is tested by using two literature 3D head models. The tracking method proposed has been compared with state of the art methods using Supervised Descent Regressors (SDR) in terms of accuracy, achieving an improvement of 60%.