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.
Lajić et al. (2026) studied this question.