Abstract
Head pose estimation represents an important computer vision technique in different contexts where image acquisition cannot be controlled by an operator, making face recognition of unknown subjects more accurate and efficient. In this work, starting from partitioned iterated function systems to identify the pose, different regression models are adopted to predict the angular value 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 experimental evaluation is performed on widely used benchmark datasets, i.e., Biwi and AFLW2000, and the results are compared with many existing state-of-the-art methods, demonstrating the robustness of the proposed fusion approach and excellent performance.
Highlights
Head pose estimation (HPE) is a computer vision technique for determining the orientation of a human’s head
In any context where image acquisition cannot be controlled by an operator, automated HPE of an unknown subject makes face recognition much more accurate and efficient [33]
The head rotation movements can be determined in different forms
Summary
Head pose estimation (HPE) is a computer vision technique for determining the orientation of a human’s head. Head movements represent an important aspect of a subject, providing several characteristics like individual’s intentions and attention. In any context where image acquisition cannot be controlled by an operator, automated HPE of an unknown subject makes face recognition much more accurate and efficient [33]. Many application systems have been developed based on the estimation of the human head directions and movements, finding applicability in several contexts, such as video surveillance and driving monitoring systems. The head rotation movements can be determined in different forms. The usually chosen representation uses the Euler angles.
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