Unfamiliar face recognition, the visual identification of a person with whom you are unfamiliar, is commonly utilized in security settings. However, our continued reliance on unfamiliar face recognition for identity verification is not supported by findings from psychological science . Research has shown that whether it be for face photos or live faces, specialists or student control groups, unfamiliar face recognition is prone to error and can be exploited by fraudsters seeking to deceive identity checkers. The selection of super-recognizers (SRs)— professionals trained in unfamiliar face recognition for security-critical roles— would appear to be the best strategy at present to improve accuracy in unfamiliar face identification. However, the selection and deployment of these individuals must be standardized, with clear criteria for SR categorization, and individual SRs must be assessed across a variety of tests (i.e., matching and memory) to ensure effective deployment. This article will review the state of the art in unfamiliar face recognition research, before discussing two newer forms of identity fraud: hyper-realistic masks and morphs. Advancements in surveillance and biometric technologies will not obviate the need for border and law enforcement agencies to have capabilities in human-based facial recognition.
|Number of pages||5|
|Specialist publication||The Journal of The United States Homeland Defence and Security Information Analysis Center (HDIAC)|
|Publication status||Published - 11 Apr 2018|
- face recognition
- face matching
- identity fraud
- identity verification