Explainable Face Image Quality Assessment
The high performance of today's face recognition systems is driven by the quality of the used samples. To ensure a high quality of face images enrolled in a system, face image quality assessment is performed on these images. However, if an image is rejected due to low quality it is not obvious to the user why. Showing what led to the low quality is useful to increase trust and transparency in the system. Previous work has never addressed the explainability of their quality estimation, but only the explainability of their face recognition approaches. In this work, we propose a gradient-based method that explains which pixels contribute to the overall image quality and which do not. By adding a quality node to the end of the model, we can calculate quality-dependent gradients and visualize them. The qualitative experiments are conducted on three different datasets and we also propose a general framework for quantitative analysis of face image quality saliency maps. With our method, we can assign quality values to individual pixels and provide a meaningful explanation of how face recognition models work and how they respond to face image quality impairments. Our method provides pixel-level explanations, requires no training, applies to any face recognition model, and also takes model-specific behavior into account. By explaining how the poor quality is caused, concrete instructions can be given to people who take pictures suitable for face recognition, face image standards can be adapted, and low-quality areas can be in painted.
Darmstadt, TU, Master Thesis, 2021