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Abstract：
Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.
摘要：
大多数面部分析方法在标准化测试中表现良好，但在实际测试中表现不佳。主要原因是训练模型无法轻松学习各种人类特征和背景噪音，特别是对于面部标志检测和头部姿态估计任务与有限和嘈杂的训练数据集。为了缓解标准化测试和真实世界测试之间的差距，我们提出了一种伪标记技术，使用由各种人和背景噪声组成的面部识别数据集。使用我们的伪标记训练数据集可以帮助克服数据集中人之间缺乏多样性的问题。我们的集成框架是使用互补的多任务学习方法构建的，可以为每个任务提取健壮的特征。此外，引入伪标记和多任务学习通过实现姿态不变特征的学习来提高人脸识别性能。我们的方法在AFLW2000-3D和BIWI数据集上实现了最先进的（SOTA）或接近SOTA的性能，用于面部标志检测和头部姿势估计，在用于人脸识别的IJB-C测试数据集上具有竞争力的人脸验证性能。我们通过一种新颖的测试方法来证明这一点，该方法将案例分类为软，中等,并且很难基于IJB-C的位姿值。即使在数据集缺乏不同的人脸识别时，该方法也能实现稳定的性能。
