Compatibility of Face Embeddings Across Deep Neural Networks
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. At the same time, large foundation models that are pretrained on broad vision or vision-language tasks have shown impressive generalization across diverse domains, including biometrics. This raises an important question: Do different DNN models---both domain-specific and foundation models---encode facial identity in similar ways, despite being trained on different datasets, loss functions, and architectures? In this regard, we directly analyze the geometric structure of embedding spaces imputed by different ...
| Authors | Fizza Rubab, Yiying Tong, Arun Ross |
|---|---|
| Primary category | cs.CV |
| Published | 8 April 2026 |
| Last updated at source | 9 August 2026 |
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