Abstract
Deep learning models perform exceptionally well on standard visual benchmarks but frequently degrade in high-stakes, low-data classification tasks. Meanwhile, trained human experts consistently demonstrate superior accuracy in highly specialized visual domains, including rare medical pathology, deepfake forensics, astrophysical morphology. Despite this operational edge, no formal framework currently exists to deploy human perception as a classification architecture. This paper introduces a formal classification framework that operationalizes expert human visual judgment using the language and rigor of machine learning. We argue that for high-stakes, low-volume tasks characterized by a verified human superiority gap, treating the trained biological visual system as a formal classifier, rather than a mere labeler, yields superior explainability, robustness, and calibration compared to deep learning.
