Nevin Manimala Statistics

Race and statistics in facial recognition: Producing types, physical attributes, and genealogies

Soc Stud Sci. 2022 Oct 27:3063127221127666. doi: 10.1177/03063127221127666. Online ahead of print.


Principal component analysis (PCA) is a common statistical procedure. In forensics, it is used in facial recognition technologies and composite sketching systems. PCA is especially helpful in contexts with high facial diversity, which is often translated as racial diversity. In these settings, researchers use PCA to define a ‘normal face’ and organize the rest of the available facial diversity based on their resemblance to or difference from that norm. In this way, the use of PCA introduces an ‘ontology of the normal’ in which expectations about how a normal face should look are corroborated by statistical calculations of normality. I argue that the use of PCA can lead to a statistical reification of racial stereotypes that informs recognition practices. I discuss current and historical cases in which PCA is used: one of face perception theorization (‘face space theory’) and two of technology development (the ‘eigenfaces’ facial recognition algorithm and the ‘EvoFIT’ composite sketching system). In each, PCA aligns facial normality with racial expectations, and instrumentalizes race in specific ways: as a type, physical attribute, or genealogy. This analysis of PCA does two things. First, it opens the black box of facial recognition to uncover how stereotypes and intuitions about normality become part of theories and technologies of facial recognition. Second, it explains why racial categorizations remain central in contemporary identification technologies and other forensic practices.

PMID:36301181 | DOI:10.1177/03063127221127666

By Nevin Manimala

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