Beyond aggregate scores: Deployment-aware and non-compensatory benchmarking of vision-based eye-state recognition models for driver monitoring
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 models were evaluated using a subject-disjoint MRL Eye protocol, deterministic image corruptions, zero-shot transfer and participant-safe target-domain training with out-of-fold evaluation on RT-BENE, TensorRT FP32 inference on an NVIDIA Jetson Nano, and...
| Authors | Ruben Dario Florez-Zela |
|---|---|
| Primary category | cs.CV |
| Published | 6 June 2026 |
| Last updated at source | 13 August 2026 |
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