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.

翻译：人脸对齐是为面部分析任务中的特征提取准备脸部图像的关键步骤。在人脸识别、面部表情识别和面部属性分类等应用中，对齐技术被广泛应用于训练和推理阶段，以标准化面部关键特征点的位置。众所周知，人脸对齐的应用方法会显著影响面部分析模型的性能。然而，对齐对脸部图像质量的影响尚未得到深入研究。当前的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，尤其是在现代基于深度学习的检测器（集成了检测与特征点定位功能）出现之后。为满足这一需求，本研究探讨了人脸对齐对脸部图像质量评分的影响。我们在LFW、IJB-B和SCFace数据集上进行了实验，采用MTCNN和RetinaFace模型进行人脸检测与对齐。为评估脸部图像质量，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。研究结果表明，脸部图像质量评估方法对对齐操作具有敏感性。此外，在具有挑战性的现实条件下，这种敏感性会进一步增强，这凸显了评估对齐在质量评估中作用的重要性。