Head pose estimation (HPE) has been widely studied in the last years due to its many applications in face analysis systems. The use of such systems ranges from the analysis of focus of attention, social interactions or the use in mobile applications in the realization of the currently popular facial animations and / or in face recognition process, where the frontal faces are especially important. Many approaches were proposed focusing mainly in Random Forests and Convolutional Neural Networks (CNN). In this paper, a framework for estimation of the head pose was proposed computing the degrees of freedom (DOF) of the human head using 2D images data only. The framework implements some computer vision algorithms available in publicly machine learning libraries such as OpenCV and Dlib, which allows easy application and re-implementation. In addition, a Support Vector Machine (SVM) model with Radial Basis Function (RBF) kernel was developed for frontal face classification. Experiments conducted on 2D image datasets in constrained environment show that the approach is capable of real-time performance. Were designed three protocols of experiments with two databases for testing the SVM model. Values of 100% and 98% for precision and recall, respectively, were achieved classifying frontal faces. Significant results were obtained measuring yaw rotation with 4.24 of mean absolute error for frontal face.

Developing software for wireless sensor networks is a task that requires certain technical knowledge about this technology. As a consequence, the potential pool of developers is constrained to those programmers who have the proper formation. This fact prevents people making use of sensor networks as an auxiliary tool for their scientific or production-related studies (who typically have a formation focused on areas such as biology, geology or agriculture) from being able to develop their own software applications. To this regard, the proposal of this PhD Thesis consists in a new software development method for wireless sensor networks, based on MDE (Model Driven Engineering), which enables the description of applications by using simple concepts and the generation of executable code form these descriptions. With this approach, there is no need of acquiring additional technical skills and, also, the development of applications is considerably simplified.