The Flexible Face: Unifying the Protocols of Facial Recognition Technologies
Bibliographic record
Abstract
“The Flexible Face: Unifying the Protocols of Facial Recognition Technologies” reconstructs the key historical constellations of technical, representational, and political protocols that have resulted in contemporary facial recognition technologies’ (FRTs) ubiquitous field of automated vision. This dissertation excavates the past 200 years including case studies such as the Nippon Electric Company’s work on the first public demonstration of FRTs in 1970, the 1990s establishment of the massive dataset FERET, and the contemporary solving of masked faces within FRTs during COVID, while also involving unique archival work from The Francis Galton Papers (London U.K.) and the papers of Woodrow “Woody” Bledsoe (University of Texas at Austin). From this historical scholarship, this dissertation argues that FRTs’ effectiveness as a biopolitical tactic is rooted in an incredible adaptability and flexibility brought about by the technology’s entwined technical, representational, and political protocols.\n\nUtilizing a three-pronged media archeological methodology, this dissertation presents a unified understanding of FRTs’ three sets of protocols working symbiotically: the technical protocols draw from vision science rooted in 19th century experimental psychology which have been expanded into deterministic and linear models of vision, powered by advancements in the science of vision, computer science, and computer vision; representational protocols, most overtly present in the facial databases used in machine learning training and operationalizing of the technology, act under predictive logics to categorize and hierarchize the faces under observation into stable data defined by difference; political protocols, in combinations of state and corporate actors under globalized capitalism, manage and control individuals and populations through the gathering and circulation of facial data and by use of the technology, often in service of a self-perpetuating hegemonic power powered by asymmetrical control of political recognition. This dissertation’s historical approach surfaces how the various formations of protocols within FRTs have depended upon, and continue to depend upon, the circulations of both top-down and bottom-up forms of power united with performances of citizenship that collapse consent and coercion within the behaviour of citizens and non-citizens in ways that manage and gatekeep resources related to citizenship, in particular during moments of crisis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.041 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".