2023 |
Babnik, Žiga; Peer, Peter; Štruc, Vitomir DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models Proceedings Article V: IEEE International Joint Conference on Biometrics , str. 1-10, IEEE, Ljubljana, Slovenia, 2023. Povzetek | Povezava | BibTeX | Oznake: biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face image quality assesment, face recognition, FIQA, quality @inproceedings{Diffiqa_2023, Modern face recognition (FR) models excel in constrained scenarios, but often suffer from decreased performance when deployed in unconstrained (real-world) environments due to uncertainties surrounding the quality of the captured facial data. Face image quality assessment (FIQA) techniques aim to mitigate these performance degradations by providing FR models with sample-quality predictions that can be used to reject low-quality samples and reduce false match errors. However, despite steady improvements, ensuring reliable quality estimates across facial images with diverse characteristics remains challenging. In this paper, we present a powerful new FIQA approach, named DifFIQA, which relies on denoising diffusion probabilistic models (DDPM) and ensures highly competitive results. The main idea behind the 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 the diffusion-based perturbations are computationally expensive, we also distill the knowledge encoded in DifFIQA into a regression-based quality predictor, called DifFIQA(R), that balances performance and execution time. We evaluate both models in comprehensive experiments on 7 diverse datasets, with 4 target FR models and against 10 state-of-the-art FIQA techniques with highly encouraging results. The source code is available from: https://github.com/LSIbabnikz/DifFIQA. |
Ivanovska, Marija; Štruc, Vitomir Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models Proceedings Article V: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), str. 1-6, 2023. Povzetek | Povezava | BibTeX | Oznake: biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face morphing attack, morphing attack, morphing attack detection @inproceedings{IWBF2023_Marija, Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion--based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets. |
Objave
2023 |
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models Proceedings Article V: IEEE International Joint Conference on Biometrics , str. 1-10, IEEE, Ljubljana, Slovenia, 2023. |
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models Proceedings Article V: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), str. 1-6, 2023. |