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Online learning of robust facial feature trackers

机译:在线学习强大的面部特征跟踪器

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This paper presents a head pose and facial feature estimation technique that works over a wide range of pose variations without a priori knowledge of the appearance of the face. Using simple LK trackers, head pose is estimated by Levenberg-Marquardt (LM) pose estimation using the feature tracking as constraints. Factored sampling and RANSAC are employed to both provide a robust pose estimate and identify tracker drift by constraining outliers in the estimation process. The system provides both a head pose estimate and the position of facial features and is capable of tracking over a wide range of head poses.
机译:本文提出了一种头部姿势和面部特征估计技术,该技术可在各种姿势变化范围内工作,而无需先验知识即可了解面部外观。使用简单的LK跟踪器,Levenberg-Marquardt(LM)姿势估计使用特征跟踪作为约束来估计头部姿势。分解采样和RANSAC既可提供鲁棒的姿态估计,又可通过限制估计过程中的异常值来识别跟踪器漂移。该系统既提供头部姿势估计值,又提供面部特征的位置,并且能够跟踪各种头部姿势。

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