The paper introduces a benchmarking framework that evaluates open-weight Vision‑Language Models (VLMs) for face recognition by treating explanation quality as a core metric. It defines two key criteria for explanations—relevance, meaning reliance on identity‑stable facial features, and faithfulness, meaning alignment with the visible image content without hallucinations. Using this framework, the authors benchmark several VLM families, jointly assessing face verification accuracy and explanation quality, and find that current models still exhibit shortcomings in their explanations, underscoring the importance of explanation metrics for a complete performance assessment.
arXiv:2608.24430v1 Announce Type: new Abstract: Responsible deployment of face verification systems requires more than accurate decisions: systems should also provide interpretable and auditable evid...
EXPL-FR is a lightweight adapter that aligns a vision‑language model’s image encoder with a frozen face‑recognition (FR) embedding space, enabling the FR model to be explained using semantic attribute prompts without any text training. By mapping 978 attribute prompts across 22 categories into the FR space, the method identifies the most detectable concepts—forming a readable semantic signature that better separates identities than the full vocabulary. The approach is evaluated on four FR backbones and two VLM encoders, providing identity‑level, per‑image, and differential explanations, and demonstrates that prompt‑driven audits can rank FR models by per‑ethnicity error and attribute‑change verification cost without requiring labeled data.
arXiv:2607. 14932v1 Announce Type: cross Abstract: Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs.
Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace.
FORGE is a forensic deepfake analysis system that provides region‑grounded natural language explanations for image manipulations. It addresses the inductive bias mismatch of multimodal large language models by adding a Vision‑Only Model trained on dense patch prediction, allowing the language model to interleave tokens with preserved spatial correspondence. Across face‑manipulated and fully synthetic content, FORGE delivers fine‑grained attribute queries and outperforms in‑domain baselines, with region‑specific evaluation and human studies confirming explanation faithfulness.
arXiv:2608. 06865v1 Announce Type: cross Abstract: The malicious use of generative artificial intelligence to create highly realistic deepfake videos raises serious ethical concerns and poses substantial challenges to AI safety.
arXiv:2608. 14130v1 Announce Type: cross Abstract: Computer vision models for generated facial content, such as face editing and privacy protection, increasingly affect people, requiring similarity metrics that serve as faithful proxies for human perception.
arXiv:2608.29802v1 Announce Type: new Abstract: Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Pre...
arXiv:2608.15404v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) are designed to make visual classification interpretable by expressing predictions through human-understandable co...
arXiv:2407. 13922v3 Announce Type: replace-cross Abstract: Face recognition (FR) systems are widely deployed in critical applications, making their reliability and robustness across diverse populations and conditions essential.
arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.
arXiv:2609.00411v1 Announce Type: new Abstract: Modern face recognition (FR) owes much of its success to deep neural networks that learn to extract compact identity embeddings from face images. These...