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Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognition

Created by
  • Haebom

Author

Redwan Sony, Parisa Farmanifard, Arun Ross, Anil K. Jain

Outline

This paper compares and analyzes the face recognition performance of general base models (e.g., CLIP, BLIP, GPT-4o, Grok-4) and specialized models (e.g., AdaFace, ArcFace). Experiments using multiple base models and benchmark datasets demonstrate that the specialized models outperform the zero-shot base model, and that the zero-shot base model improves on over-segmented face images. Furthermore, score-level fusion of the base and specialized models improves accuracy at low error rates. Furthermore, base models such as GPT-4o and Grok-4 provide explainability for the face recognition pipeline and help address the low confidence decision-making of AdaFace. In conclusion, we emphasize the importance of appropriately combining specialized and base models.

Takeaways, Limitations

Takeaways:
We demonstrate that the specialized facial recognition model outperforms the zero-shot baseline model.
Suggesting the importance of contextual information in over-segmented images.
Suggesting the possibility of performance improvement through score level fusion of basic and specialized models.
Suggesting the possibility of securing explainability and improving reliability of facial recognition pipelines using basic models.
Limitations:
Experimental results limited to specific base and specialized models and datasets.
Further research is needed on other fusion methods besides score-level fusion.
Lack of robust assessment of various facial features (e.g., expression, lighting).
Lack of quantitative assessment of the explanatory potential of the underlying model.
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