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Pose estimation system of 3-D human face using nearest feature line in its eigenspace representation

机译：利用特征空间中最接近特征线的3-D人脸姿态估计系统

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In this paper, a pose estimation system is developed using a minimum distance calculation of projected unknown viewpoint of a spatial image into its eigenspace representation to the nearest line of the two known viewpoints in the same eigenspace representation. In order to have a higher recognition rate on determining the pose position of the unknown image, we developed FullyK-LT and SubsetK-LT methods. The developed system is performed to determine the pose position of 2-D images taken from the human model by gradually changing visual points, which is done by successively varying the camera position from -90 to +90 with an interval of 15 degree. The experimental results shown that the highest recognition rate of the system is about 57.5% when using FullyK-LT method, and could be increased up to 93.8% when using SubsetK-LT method.
机译：在本文中，一种姿态估计系统是通过将空间图像投影到其特征空间表示中的已知未知视点的距离最小的计算而开发出来的，该最小距离计算是同一特征空间表示中两个已知视点的最接近线。为了在确定未知图像的姿势位置时具有较高的识别率，我们开发了FullyK-LT和SubsetK-LT方法。通过逐步改变视点来执行开发的系统，以确定从人体模型拍摄的2D图像的姿势位置，这是通过将相机位置从-90到+90连续以15度的间隔进行更改来完成的。实验结果表明，使用FullyK-LT方法时，系统的最高识别率约为57.5％，使用SubsetK-LT方法时，系统的最高识别率可提高到93.8％。

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