Loading [a11y]/accessibility-menu.js
eDifFIQA: Towards Efficient Face Image Quality Assessment Based on Denoising Diffusion Probabilistic Models | IEEE Journals & Magazine | IEEE Xplore

eDifFIQA: Towards Efficient Face Image Quality Assessment Based on Denoising Diffusion Probabilistic Models


Abstract:

State-of-the-art Face Recognition (FR) models perform well in constrained scenarios, but frequently fail in difficult real-world scenarios, when no quality guarantees can...Show More

Abstract:

State-of-the-art Face Recognition (FR) models perform well in constrained scenarios, but frequently fail in difficult real-world scenarios, when no quality guarantees can be made for face samples. For this reason, Face Image Quality Assessment (FIQA) techniques are often used by FR systems, to provide quality estimates of captured face samples. The quality estimate provided by FIQA techniques can be used by the FR system to reject samples of low-quality, in turn improving the performance of the system and reducing the number of critical false-match errors. However, despite steady improvements, ensuring a good trade-off between the performance and computational complexity of FIQA methods across diverse face samples remains challenging. In this paper, we present DifFIQA, a powerful unsupervised approach for quality assessment based on the popular denoising diffusion probabilistic models (DDPMs) and the extended (eDifFIQA) approach. The main idea of the base DifFIQA approach is to utilize the forward and backward processes of DDPMs to perturb facial images and quantify the impact of these perturbations on the corresponding image embeddings for quality prediction. Because of the iterative nature of DDPMs the base DifFIQA approach is extremely computationally expensive. Using eDifFIQA we are able to improve on both the performance and computational complexity of the base DifFIQA approach, by employing label optimized knowledge distillation. In this process, quality information inferred by DifFIQA is distilled into a quality-regression model. During the distillation process we use an additional source of quality information hidden in the relative position of the embedding to further improve the predictive capabilities of the underlying regression model. By choosing different feature extraction backbone models as the basis for the quality-regression eDifFIQA model, we are able to control the trade-off between the predictive capabilities and computational complexity of the ...
Page(s): 458 - 474
Date of Publication: 12 March 2024
Electronic ISSN: 2637-6407

Funding Agency:


References

References is not available for this document.