Accuracy Enhancement in Face-pose Estimation Network Using Incrementally Updated Face-shape Parameters
Accuracy Enhancement in Face-pose Estimation Network Using Incrementally Updated Face-shape Parameters
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DOI：
10.1109/ur49135.2020.9144866
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Makoto Sei;A. Utsumi;H. Yamazoe;Joo-Ho Lee
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文献类型：
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作者：
Makoto Sei;A. Utsumi;H. Yamazoe;Joo-Ho Lee
在本文中，我们追求一种改进的人脸姿势估计方法，该方法使用增量更新的人脸形状参数。基于网络的算法一般依赖于使用大数据集的在线训练过程，并且训练后的网络通常以一次一次的方式工作，即利用静态网络逐个处理输入图像。另一方面，在许多实际应用中，我们期望顺序观测而不仅仅是单图像观测具有很大的优势。在这种情况下，动态使用多个观测值有助于提高系统性能。因此，在我们之前的研究中，我们在基于网络的人脸姿势估计方法中引入了一种基于序贯观测的增量式个性化机制，其中迭代人脸形状估计中的平均参数被用于人脸姿势估计。该方法的头部姿态估计精度约为2度。本文通过实验考察了人脸形状估计的误差分布，并讨论了一种有效的基于误差分布的增量个性化机制来更新人脸形状参数。
In this paper, we pursue the refinement of a face-pose estimation method using incrementally updated face-shape parameters. Network-based algorithms generally rely on an on-line training process that uses a large dataset, and a trained network usually works in a one-shot manner, i.e., each input image is processed one by one with a static network. On the other hand, we expect a great advantage from having sequential observations, rather than just single-image observations, in many practical applications. In such cases, the dynamic use of multiple observations can contribute to improving system performance. In our previous study, therefore, we introduced an incremental personalization mechanism using sequential observations to a network-based face-pose estimation method, where the averaged parameters in iterative face-shape estimations are used for face-pose estimation. The head pose estimation accuracy of our method was about 2 degrees. In this paper, we conduct an experiment to examine the error distribution of face-shape estimation and discuss an effective incremental personalization mechanism to update the face-shape parameters based on the error distribution.