School of Electrical and Electronic Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea.
School of Computer Science and Engineering, Kunsan National University, 558 Daehak-ro, Gunsan 54150, Jeollabuk-do, Republic of Korea.
Sensors (Basel). 2024 May 18;24(10):3212. doi: 10.3390/s24103212.
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.
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难以轻松学习各种人类特征和背景噪声，特别是对于面部地标检测和头部姿势估计任务，这些任务的训练数据集有限且存在噪声。为了缩小标准化测试和实际测试之间的差距，我们提出了一种使用包含各种人和背景噪声的人脸识别数据集的伪标签技术。使用我们的伪标签训练数据集可以帮助克服数据集中人员多样性不足的问题。我们的集成框架使用互补的多任务学习方法构建，以提取每个任务的稳健特征。此外，引入伪标签和多任务学习可以通过学习不变特征来提高人脸识别性能。我们的方法在 AFLW2000-3D 和 BIWI 数据集上的面部地标检测和头部姿势估计方面实现了最先进（SOTA）或接近 SOTA 的性能，在 IJB-C 测试数据集上的人脸识别方面具有竞争力的人脸验证性能。我们通过一种新的测试方法证明了这一点，该方法根据 IJB-C 的姿势值将情况分为软、中、硬三类。即使数据集缺乏多样化的人脸识别，该方法也能保持稳定的性能。
