Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique changes, the performance of most existing FIQA techniques quickly becomes suboptimal. To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures. To validate the proposed distillation approach, we conduct comprehensive experiments on 6 face datasets with 4 recent face recognition models and in comparison to 7 state-of-the-art FIQA techniques. Our results show that AI-KD consistently improves performance of the initial FIQA techniques not only with misaligned samples, but also with properly aligned facial images. Furthermore, it leads to a new state-of-the-art, when used with a competitive initial FIQA approach. The code for AI-KD is made publicly available from: https://github.com/LSIbabnikz/AI-KD.

翻译：摘要：近年来，人脸图像质量评估（FIQA）技术取得了稳步进展，但若输入人脸样本未进行适当对齐，其性能仍会下降。这种对齐敏感性源于大多数FIQA技术是基于特定人脸对齐流程训练或设计的。若对齐方法发生变化，现有FIQA技术的性能会迅速变得不理想。为解决该问题，本文提出了一种新颖的知识蒸馏方法——AI-KD，该方法可扩展至任何现有FIQA技术，提升其对对齐变化的鲁棒性，进而改善不同对齐流程下的性能。为验证所提出的蒸馏方法，我们在6个人脸数据集上，与4个近期人脸识别模型及7种最新FIQA技术进行了全面实验。结果表明，AI-KD不仅能提升原始FIQA技术在未对齐样本上的性能，还能扩展至对齐良好的面部图像，实现性能提升。此外，当与具备竞争力的初始FIQA方法结合使用时，该方法可达到新的最优性能。AI-KD的代码已公开于：https://github.com/LSIbabnikz/AI-KD。