Variance of Local Contribution: an Unsupervised Image Quality Assessment for Face Recognition
Qiye Lian, Xiaohua Xie, Huicheng Zheng, Yongdong Zhang · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
In recent years, Face Image Quality Assessment (FIQA) plays an important role in the face recognition system. However, how to define face image quality is still an open question. In this work, we argue that a high-quality face image should have more identity-related information than a low-quality face image. Thus, we propose a novel unsupervised Face Image Quality Assessment with the variance of local contribution (VLC-FIQA). In our approach, we alternately mask partial pixels of the face image, then quantify the importance of these pixels and compute the variation of the importance of different parts as the quality of the image. Extensive experiments show that our VLC-FIQA outperforms state-of-the-art approaches on LFW. Our approach can be easily used for any recognition system and be extended to other recognition tasks such as person re-identification.