Beyond aggregate scores: Deployment-aware and non-compensatory benchmarking of vision-based eye-state recognition models for driver monitoring

Excerpt

arXiv:2606.08123v2 Announce Type: replace-cross Abstract: Model selection for safety-relevant visual recognition is often based on clean aggregate performance, although robustness, transfer, embedded latency, and explanation faithfulness may produce different preferences. This study presents a Human-Centered Benchmarking Framework (HCBF) that separates multidimensional evidence from non-compensatory operational eligibility. Six compact convolutional and transformer-oriented eye-state recognition