arXiv Machine Learning By Fizza Rubab, Yiying Tong, Arun Ross

Compatibility of Face Embeddings Across Deep Neural Networks

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arXiv:2604. 07282v2 Announce Type: replace-cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be trained for domain-specific tasks.

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arXiv Computer Vision
Aug 25

EXPL-FR: Explaining Face Recognition Models via Vision-Language Alignment

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.

By Guray Ozgur, Mustafa Efe Tamyapar, Naser Damer, Fadi Boutros