StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.
Comparing the model’s predictions with human preferences yielded an accuracy of 0.707, Cohen’s𝜅 of 0.392, and MCC of 0.402 [Landis and Koch 1977; Matthews 1975]
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StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
StyleID supplies human-perception-aligned benchmarks and fine-tuned encoders that improve facial identity recognition robustness across stylization types and strengths.