Multi-view Geometry Consistency Network for Facial Micro-Expression Recognition From Various Perspectives
Multi-view Geometry Consistency Network for Facial Micro-Expression Recognition From Various Perspectives
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DOI：
10.1109/ijcnn55064.2022.9892565
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Devarth Parikh;Yawen Lu;N. Kasabov;G. Lu
中科院分区：
文献类型：
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作者：
Devarth Parikh;Yawen Lu;N. Kasabov;G. Lu
凝视估计在人类注意力认识，人类行为分析和增强现实应用中起着至关重要的作用。大多数基于神经网络的深度凝视估计技术都采用监督学习来提取特征并直接回归3D注视向量，从而导致高劳动力成本的脆弱性和有限的概括。在这项工作中，我们提出了一种弱监督的方法，以共同优化具有多视图几何约束的眼标和相对姿势的深度值，以确定观察者的最终目光向量。具体而言，我们以顺序的眼睛区域图像为食，并设计一个深度回归网络以估计眼睛区域标志的深度，姿势估计网络进一步利用了姿势估计网络，以估计视力向量在具有多视图几何约束中的相对变化。虹膜地区。合成和真实数据的实验表明，所提出的方法是可行的，并且有望在没有强大姿势监督的情况下学习凝视估计。
Gaze estimation plays an essential role in human attention recognition, human behavior analysis and augmented reality applications. Most of the deep neural network-based gaze estimation techniques apply supervised learning to extract features and regress 3D gaze vectors directly, leading to a vulnerability of high labor cost and limited generalization. In this work, we proposed a weakly-supervised method to jointly optimize the depth values of eye landmarks and relative poses with a multi-view geometric constraint to determine the final gaze vectors of observers. Specifically, we feed in sequential eye region images, and design a depth regression network to estimate the depth of the eye region landmarks, which are further utilized by the pose estimation network to estimate the relative changes of gaze vectors with multi-view geometric constraints in the iris regions. Experiments on both synthetic and real data show that the proposed method is feasible and promising to learn gaze estimation without strong pose supervision.