2026 |
Lajić, Romanela; Peer, Peter; Štruc, Vitomir; Han, Dong Seog; Meden, Blaž; Emeršič, Žiga FACES: Facial Analysis with Compressed Efficient Systems Journal Article In: ICT Express, 2026. Abstract | Links | BibTeX | Tags: biometrics, distillation, face recognition, knowledge distillation @article{Romanela_ICT_Express,Due to their promising performance vision transformers are increasingly being incorporated into various biometric solutions, mainly in the domain of face analysis. However, their size and computational expense remain the biggest challenge when it comes to their full utilization and there is a high demand for optimization of these models. In this paper we propose a novel pruning technique for face analysis vision transformers aimed at reducing their memory and computational cost. The method uses existing transformer parameters as importance scores, which allows for a simple one-shot pruning and retraining approach. By testing the method on the SWINFace transformer for both verification and attribute recognition tasks, we show that the models compressed up to 50% sparsity level maintain the performance or even outperform the original model, while also outperforming state-of-the-art vision transformer pruning methods and showing versatility for different face analysis tasks. |
2023 |
Babnik, Žiga; Damer, Naser; Štruc, Vitomir Optimization-Based Improvement of Face Image Quality Assessment Techniques Proceedings Article In: Proceedings of the International Workshop on Biometrics and Forensics (IWBF), 2023. Abstract | Links | BibTeX | Tags: distillation, face, face image quality assessment, face image quality estimation, face images, optimization, quality, transfer learning @inproceedings{iwbf2023babnik,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. |