Reinforcement Training of Face Recognition Systems Using Morphing and XAI Methods

Abstract

This study introduces a reinforcement training framework for face recognition systems (FRSs) that leverages facial morphing techniques to generate counterfactual visual instances for model enhancement. Two complementary morphing strategies were employed: a geometric approach based on Delaunay–Voronoi triangulation (DVT-Morph) and a generative approach using latent diffusion and autoencoder-based models (diffusion-based morphing [MorDIFF]). The generated morphs act as controlled counterfactuals, representing minimally modified facial images that induce changes in FRS verification decisions. The proposed method integrates these counterfactuals into the training process of two state-of-the-art recognition systems, ArcFace and MagFace, to strengthen their decision boundaries and improve their robustness, calibration, and explainability. By combining morphing-based counterfactual generation with eXplainable Artificial Intelligence (XAI) techniques, the framework enables a more interpretable embedding space and increased resilience against morphing and adversarial perturbations. The experimental results demonstrate that the inclusion of morph-based counterfactuals significantly enhances the verification accuracy and decision transparency of modern FRSs. Moreover, the methodology is model- and morphing-agnostic and can be applied to any FRS architecture, regardless of the morphing generation technique.