论文标题
使用3D面部标志的大型表达变化的主题识别
Subject Identification Across Large Expression Variations Using 3D Facial Landmarks
论文作者
论文摘要
具有里程碑意义的定位是迈向基于几何视力研究的重要第一步，包括主题识别。考虑到这一点，我们建议在一系列表达的情绪上使用3D面部地标作为主题识别的任务。使用时间可变形的形状模型检测到地标，并用于训练支持向量机（SVM），随机森林（RF）和长期记忆（LSTM）神经网络进行受试者识别。由于我们对表达差异很大的主题识别感兴趣，因此我们对3个基于情感的数据库进行了实验，即BU-4DFE，BP4D和BP4D+ 3D/4D脸部数据库。我们表明，我们所提出的方法的表现优于BU-4DFE和BP4D上的主题识别的当前状态。据我们所知，这是研究BP4D+的主题识别的第一项工作，从而为社区提供了基准。
Landmark localization is an important first step towards geometric based vision research including subject identification. Considering this, we propose to use 3D facial landmarks for the task of subject identification, over a range of expressed emotion. Landmarks are detected, using a Temporal Deformable Shape Model and used to train a Support Vector Machine (SVM), Random Forest (RF), and Long Short-term Memory (LSTM) neural network for subject identification. As we are interested in subject identification with large variations in expression, we conducted experiments on 3 emotion-based databases, namely the BU-4DFE, BP4D, and BP4D+ 3D/4D face databases. We show that our proposed method outperforms current state of the art methods for subject identification on BU-4DFE and BP4D. To the best of our knowledge, this is the first work to investigate subject identification on the BP4D+, resulting in a baseline for the community.
