Partitioned iterated function systems by regression models for head pose estimation

Head pose estimation represents an important computer vision technique in different contexts where image acquisition cannotbe controlled by an operator, making face recognition of unknown subjects more accurate and efficient. In this work, startingfrom partitioned iterated function systems to identify the pose, different regression models are adopted to predict the angularvalue errors (yaw, pitch and roll axes, respectively). This method combines the fractal image compression characteristics,such as self-similar structures in order to identify similar head rotation, with regression analysis prediction. The experimentalevaluation is performed on widely used benchmark datasets, i.e., Biwi and AFLW2000, and the results are compared with manyexisting state-of-the-art methods, demonstrating the robustness of the proposed fusion approach and excellent performance.