2026 |
Kolf, Jan Niklas; Ozgur, Guray; Atzori, Andrea; Babnik, Žiga; Štruc, Vitomir; Damer, Naser; Boutros, Fadi PreFIQs: Face Image Quality Is What Survives Pruning Proceedings Article V: Proceedings of CVPR Workshops 2026 - CVPR Biometrics Workshop, str. 1–11, 2026. Povzetek | Povezava | BibTeX | Oznake: biometrics, deep learning, face image quality assessment, face quality, face recognition, FIQA @inproceedings{PreFIQCVPRW,Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs, an unsupervised and training-free FIQA framework grounded in the Pruning Identified Exemplar (PIE) hypothesis. We hypothesize that low-utility face images rely disproportionately on fragile network parameters, resulting in larger geometric displacement of their embeddings under model sparsification. Accordingly, PreFIQs quantifies image utility as the Euclidean distance between L2-normalized embeddings extracted from a pre-trained FR model and its pruned counterpart. We provide a first-order theoretical justification via a Jacobian-vector product analysis, demonstrating that this empirical drift serves as a computationally efficient approximation of the exact geometric sensitivity of the latent embedding manifold. Extensive experiments across eight benchmarks and four FR models demonstrate that PreFIQs achieves competitive or superior performance compared to state-of-the-art FIQA methods, including establishing new state-of-the-art results on several benchmarks, without any training or supervision. These results validate parameter sparsification as a principled and practically efficient signal for face image utility, and demonstrate that quality is, in essence, what survives pruning. Code available at https://github.com/jankolf/PreFIQs. |
2022 |
Babnik, Žiga; Štruc, Vitomir Iterativna optimizacija ocen kakovosti slikovnih podatkov v sistemih za razpoznavanje obrazov Proceedings Article V: Proceedings of ERK 2022, str. 1-4, 2022. Povzetek | Povezava | BibTeX | Oznake: CNN, face image quality estimation, face quality, face recognition, optimization, supervised quality estimation @inproceedings{BabnikErk2022,While recent face recognition (FR) systems achieve excellent results in many deployment scenarios, their performance in challenging real-world settings is still under question. For this reason, face image quality assessment (FIQA) techniques aim to support FR systems, by providing them with sample quality information that can be used to reject poor quality data unsuitable for recognition purposes. Several groups of FIQA methods relying on different concepts have been proposed in the literature, all of which can be used for generating quality scores of facial images that can serve as pseudo ground-truth (quality) labels and be exploited for training (regression-based) quality estimation models. Several FIQA approaches show that a significant amount of sample-quality information can be extracted from mated similarity-score distributions generated with some face matcher. Based on this insight, we propose in this paper a quality label optimization approach, which incorporates sample-quality information from mated-pair similarities into quality predictions of existing off-the-shelf FIQA techniques. We evaluate the proposed approach using three state-of-the-art FIQA methods over three diverse datasets. The results of our experiments show that the proposed optimization procedure heavily depends on the number of executed optimization iterations. At ten iterations, the approach seems to perform the best, consistently outperforming the base quality scores of the three FIQA methods, chosen for the experiments. |
Objave
2026 |
PreFIQs: Face Image Quality Is What Survives Pruning Proceedings Article V: Proceedings of CVPR Workshops 2026 - CVPR Biometrics Workshop, str. 1–11, 2026. |
2022 |
Iterativna optimizacija ocen kakovosti slikovnih podatkov v sistemih za razpoznavanje obrazov Proceedings Article V: Proceedings of ERK 2022, str. 1-4, 2022. |