Personalized face-pose estimation network using incrementally updated face shape parameters
Personalized face-pose estimation network using incrementally updated face shape parameters
复制标题
使用增量更新的面部形状参数的个性化面部姿势估计网络
DOI：
10.1007/s10489-021-02888-0
复制
发表时间：
2022
影响因子：
5.3
通讯作者：
Joo-Ho Lee
中科院分区：
文献类型：
--
作者：
Makoto Sei;Akira Utsumi;Hirotake Yamazoe;Joo-Ho Lee
本文提出了一种用于人类图像处理的深度学习方法，该方法结合了一种更新目标特定参数的机制。其目的是在可以连续观察目标的情况下提高系统性能。基于网络的算法通常依赖于使用大型数据集的离线训练过程，而训练有素的网络通常以一次操作的方式运行。也就是说，在静态网络中逐个处理每个输入图像。另一方面，许多实际应用可以预期使用连续观察而不是单一图像的观察。该方法通过动态使用多个观测值来提高系统性能。通过在人脸姿态估计中的应用，验证了该方法采用迭代更新过程的有效性。该方法包括两个独立的过程：1)人脸形状参数(目标特定参数)的顺序估计和更新；2)使用更新后的参数对每一幅图像的人脸姿态进行估计。实验结果表明了该方法的有效性。
In this paper, a deep learning method is proposed for human image processing that incorporates a mechanism to update target-specific parameters. The aim is to improve system performance in situations where the target can be continuously observed. Network-based algorithms typically rely on offline training processes that use large datasets, while trained networks typically operate in a one-shot fashion. That is, each input image is processed one by one in the static network. On the other hand, many practical applications can be expected to use continuous observation rather than observation of a single image. The proposed method employs dynamic use of multiple observations to improve system performance. In this paper, the effectiveness of the proposed method adopting an iterative update process is clarified through its implementation in the task of face-pose estimation. The method consists of two separate processes: 1) sequential estimation and updating of face-shape parameters (target-specific parameters) and 2) face-pose estimation for every single image using the updated parameters. Experimental results indicate the effectiveness of the proposed method.