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Face Landmark Point Tracking Using LK Pyramid Optical Flow

机译:使用LK金字塔光流的人脸地标点跟踪

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LK pyramid optical flow is an effective method to implement object tracking in a video. It is used for face landmark point tracking in a video in the paper. The landmark points, i.e. outer corner of left eye, inner corner of left eye, inner corner of right eye, outer corner of right eye, tip of a nose, left corner of mouth, right corner of mouth, are considered. It is in the first frame that the landmark points are marked by hand. For subsequent frames, performance of tracking is analyzed. Two kinds of conditions are considered, i.e. single factors such as normalized case, pose variation and slowly moving, expression variation, illumination variation, occlusion, front face and rapidly moving, pose face and rapidly moving, and combination of the factors such as pose and illumination variation, pose and expression variation, pose variation and occlusion, illumination and expression variation, expression variation and occlusion. Global measures and local ones are introduced to evaluate performance of tracking under different factors or combination of the factors. The global measures contain the number of images aligned successfully, average alignment error, the number of images aligned before failure, and the local ones contain the number of images aligned successfully for components of a face, average alignment error for the components. To testify performance of tracking for face landmark points under different cases, tests are carried out for image sequences gathered by us. Results show that the LK pyramid optical flow method can implement face landmark point tracking under normalized case, expression variation, illumination variation which does not affect facial details, pose variation, and that different factors or combination of the factors have different effect on performance of alignment for different landmark points.
机译:LK金字塔光流是在视频中实现对象跟踪的有效方法。本文中的视频用于面部界标点跟踪。考虑标志性点,即左眼的外角,左眼的内角,右眼的内角,右眼的外角,鼻尖,嘴的左角,嘴的右角。在第一帧中用手标记地标点。对于后续帧,将分析跟踪性能。考虑两种条件,即,诸如归一化情况,姿势变化和缓慢移动,表情变化,照度变化,遮挡,前脸和快速移动,姿势脸和快速移动等单个因素,以及姿势和运动等因素的组合。光照变化,姿势和表情变化,姿势变化和遮挡,光照和表情变化,表情变化和遮挡。引入全局度量和局部度量以评估在不同因素或因素组合下的跟踪性能。全局度量包含成功对齐的图像数量,平均对齐错误,失败前对齐的图像数量,而局部度量包含针对面部组件成功对齐的图像数量,组件的平均对齐错误。为了证明在不同情况下对人脸界标点的跟踪性能,我们对由我们收集的图像序列进行了测试。结果表明,LK金字塔光流法可以在归一化情况,表情变化,不影响面部细节的照度变化,姿势变化等情况下实现人脸界标点跟踪,不同因素或因素组合对对准性能的影响不同。对于不同的地标点。

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