人脸对齐对人脸图像质量的影响
Impact of Face Alignment on Face Image Quality
AI总结：
本研究探究人脸对齐对人脸图像质量评估的影响，在LFW、IJB-B和SCFace数据集上使用MTCNN和RetinaFace进行检测对齐，并用SER-FIQ等方法评估，发现质量评估对对齐敏感且在现实条件下更显著。
摘要：
人脸对齐是在人脸分析任务中准备人脸图像以进行特征提取的关键步骤。对于人脸识别、面部表情识别和人脸属性分类等应用，在训练和推理过程中广泛使用对齐来标准化面部关键地标的位置。众所周知，人脸对齐的应用和方法会显著影响人脸分析模型的性能。然而，对齐对人脸图像质量的影响尚未得到深入研究。当前的人脸图像质量评估（FIQA）研究通常将对齐视为前提条件，但并未明确评估对齐如何影响质量指标，尤其是随着现代基于深度学习的人脸检测器集成检测和地标定位的出现。为了解决这一需求，我们的研究考察了人脸对齐对人脸图像质量分数的影响。我们在LFW、IJB-B和SCFace数据集上进行了实验，使用MTCNN和RetinaFace模型进行人脸检测和对齐。为了评估人脸图像质量，我们使用了多种评估方法，包括SER-FIQ、FaceQAN、DifFIQA和SDD-FIQA。我们的分析包括检查LFW和IJB-B数据集的质量分数分布，以及分析SCFace数据集中不同距离下的平均质量分数。我们的研究结果表明，人脸图像质量评估方法对对齐敏感。此外，在具有挑战性的现实生活条件下，这种敏感性会增加，这凸显了在对齐在质量评估中的作用进行评估的重要性。
英文摘要：
Face alignment is a crucial step in preparing face images for feature extraction in facial analysis tasks. For applications such as face recognition, facial expression recognition, and facial attribute classification, alignment is widely utilized during both training and inference to standardize the positions of key landmarks in the face. It is well known that the application and method of face alignment significantly affect the performance of facial analysis models. However, the impact of alignment on face image quality has not been thoroughly investigated. Current FIQA studies often assume alignment as a prerequisite but do not explicitly evaluate how alignment affects quality metrics, especially with the advent of modern deep learning-based detectors that integrate detection and landmark localization. To address this need, our study examines the impact of face alignment on face image quality scores. We conducted experiments on the LFW, IJB-B, and SCFace datasets, employing MTCNN and RetinaFace models for face detection and alignment. To evaluate face image quality, we utilized several assessment methods, including SER-FIQ, FaceQAN, DifFIQA, and SDD-FIQA. Our analysis included examining quality score distributions for the LFW and IJB-B datasets and analyzing average quality scores at varying distances in the SCFace dataset. Our findings reveal that face image quality assessment methods are sensitive to alignment. Moreover, this sensitivity increases under challenging real-life conditions, highlighting the importance of evaluating alignment's role in quality assessment.
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