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@inproceedings{Babnik_IWBF2024,

title = {AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation},

author = {Žiga Babnik and Fadi Boutros and Naser Damer and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/03/iwbf2024_fiq.pdf},

year  = {2024},

date = {2024-04-10},

urldate = {2024-04-10},

booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},

pages = {1-6},

abstract = {Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique changes, the performance of most existing FIQA techniques quickly becomes suboptimal. To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures. To validate the proposed distillation approach, we conduct comprehensive experiments on 6 face datasets with 4 recent face recognition models and in comparison to 7 state-of-the-art FIQA techniques. Our results show that AI-KD consistently improves performance of the initial FIQA techniques not only with misaligned samples, but also with properly aligned facial images. Furthermore, it leads to a new state-of-the-art, when used with a competitive initial FIQA approach. The code for AI-KD is made publicly available from: https://github.com/LSIbabnikz/AI-KD.},

keywords = {ai, CNN, deep learning, face, face image quality assessment, face image quality estimation, face images, face recognition, face verification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{iwbf2023babnik,

title = {Optimization-Based Improvement of Face Image Quality Assessment Techniques},

author = {Žiga Babnik and Naser Damer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/03/IWBF_23___paper-1.pdf},

year  = {2023},

date = {2023-02-28},

booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},

abstract = {Contemporary face recognition~(FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (real-world) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process. While existing FIQA techniques are able to efficiently capture the differences between high and low quality images, they typically cannot fully distinguish between images of similar quality, leading to lower performance in many scenarios. To address this issue, we present in this paper a supervised quality-label optimization approach, aimed at improving the performance of existing FIQA techniques. The developed optimization procedure infuses additional information (computed with a selected FR model) into the initial quality scores generated with a given FIQA technique to produce better estimates of the ``actual'' image quality. We evaluate the proposed approach in comprehensive experiments with six  state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace, SER-FIQ) on five commonly used benchmarks (LFW, CFP-FP, CPLFW, CALFW, XQLFW) using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with highly encouraging results. },

keywords = {distillation, face, face image quality assessment, face image quality estimation, face images, optimization, quality, transfer learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BabnikErk2022,

title = {Iterativna optimizacija ocen kakovosti slikovnih podatkov v sistemih za razpoznavanje obrazov},

author = {Žiga Babnik and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/08/ERK_2022.pdf},

year  = {2022},

date = {2022-08-01},

booktitle = {Proceedings of ERK 2022},

pages = {1-4},

abstract = {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.},

keywords = {CNN, face image quality estimation, face quality, face recognition, optimization, supervised quality estimation},

pubstate = {published},

tppubtype = {inproceedings}

}