Head pose estimation is a very in-depth topic in the context of biometric recognition, especially in video surveillance, because the rotation of the head can affect the recognition of some features of the face. Being able to recognize in advance the pose of the head in pitch, yaw and roll enable frontalization or the extraction of a frame in which a face is frontal in order to allow a more accurate recognition. In this work the Web-Shaped Model algorithm is used for a coding of the pose of the face and then we apply regression algorithms to predict the pose of the face. The proposed approach stimulates the sensitivity of the regression methods to identify the head pose estimation. The goals is to predict the value of the dependent variable for the three angular values, for which some information relating to the explanatory variables is available, in order to estimate the effect on the dependent variable. The presented method is tested on some of the most well-known datasets for the head pose estimation as Biwi, AFLW2000 and Pointing’04 and compared with the various state of the art methods that use these datasets.

在生物特征识别的背景下，尤其是在视频监控中，头部姿势估计是一个非常深入的主题，因为头部的旋转会影响对面部某些特征的识别。能够预先识别头部的姿态，偏航和侧倾，能够正面化或提取面部正面的框架，从而能够更准确地识别。在这项工作中，将Web形状模型算法用于面部姿势的编码，然后我们应用回归算法来预测面部姿势。所提出的方法激发了回归方法识别头部姿势估计的敏感性。目的是针对三个角度值预测因变量的值，为此可以获得一些与解释变量有关的信息，为了估计对因变量的影响。在一些最知名的头部姿势估计数据集（例如Biwi，AFLW2000和Pointing'04）上测试了所提出的方法，并与使用这些数据集的各种最新方法进行了比较。