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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%.

When performing eye detection in a driving scenario, new challenges arise that do not occur in a standard indoor eye tracking session. Rapid subject movement, non-controlled fast light variation and partial or total occlusions are the main problems that must be overcome. Furthermore, sunlight's infrared component makes it difficult the use of active artificial infrared light sources. In this paper, we describe a novel algorithm that combines Viola Jones face detector and TLD (Tracking Learning Detection) algorithm. In a standard driving scenario, it achieves a 84% rate of detection. Furthermore, we have designed a filtering stage that allows a low false positive rate. The algorithms hardware requirement is a standard web cam, and it can potentially work in real time.This section is limited to the following 16 terms and MUST be included on the first page of all submissions after the ACM Categories section, then as well chosen properly on the Proceedings or Publication's submission page: Algorithms,