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.

头部姿态估计代表了一种重要的计算机视觉技术，在图像采集无法由操作员控制的不同环境中，使未知对象的人脸识别更加准确和高效。在这项工作中，从分区迭代函数系统开始识别位姿，采用不同的回归模型来预测角值误差（分别为偏航、俯仰和滚转轴）。该方法将分形图像压缩特征（例如自相似结构以识别相似的头部旋转）与回归分析预测相结合。实验评估是在广泛使用的基准数据集上进行的，即 Biwi 和 AFLW2000，并将结果与​​许多现有的最先进方法进行比较，