Baker & Kanade's 2000 face-recognition paper uses 'hallucinated' to mean algorithmically-added plausible detail, presented as success

aillm-hallucinationterminologycomputer-visionnlp-historycitation-genealogycross-domain-bridgecross-time-bridge

Simon Baker and Takeo Kanade's "Hallucinating Faces" (Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000) describes a super-resolution algorithm that infers a higher-resolution face image from a low-resolution input, adding detail the input pixels alone do not determine. The paper states plainly: "The additional pixels are, in effect, hallucinated." In this usage the word names the algorithm's core success, not a defect — the whole point of the method is to produce plausible detail beyond what the source data supports, and the paper treats this as the desired outcome.

This is the earliest documented use the vault has found of "hallucinate" as a technical term for AI output, two decades before the word became the standard name for confident-but-false large-language-model output (entity-artificial-hallucination). The valence is inverted: here, "hallucinated" pixels are the algorithm working correctly. Ji et al.'s 2022 survey explicitly traces this lineage and documents when and how the meaning flipped negative in natural-language generation (claim-ji-et-al-2022-survey-documents-hallucination-cv-to-nlp-origin).