100% Accuracy in automatic face recognition

R. Jenkins*, A. M. Burton

*Corresponding author for this work

Research output: Contribution to journalShort surveyResearchpeer-review

122 Citations (Scopus)


Accurate face recognition is critical for many security applications. Current automatic face-recognition systems are defeated by natural changes in lighting and pose, which often affect face images more profoundly than changes in identity. The only system that can reliably cope with such variability is a human observer who is familiar with the faces concerned. We modeled human familiarity by using image averaging to derive stable face representations from naturally varying photographs. This simple procedure increased the accuracy of an industry standard face-recognition algorithm from 54% to 100%, bringing the robust performance of a familiar human to an automated system.
Original languageEnglish
Pages (from-to)435
Number of pages1
Issue number5862
Publication statusPublished - 25 Jan 2008
Externally publishedYes


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