Human and Automated Facial Recognition Performance: A Preliminary Assessment

Abstract

Human facial recognition (FR) performance has received a substantial amount of attention from the field of Cognitive Science. Previous research has identified that the assessment of live performance (i.e., real life or operational) of human operators conducting FR tasks is limited (Kemp, Towell & Pike, 1997; Megreya & Burton, 2008). Currently human operators are being replaced by automated access control systems that rely on computer algorithms to perform FR tasks. This practice may be a cause for concern given that there is minimal evidence to support the assumption that computer FR algorithms are able to perform the task more accurately than human operators (e.g., Adler & Schuckers, 2007). This paper presents a methodology and preliminary results that incorporate the live assessment of human operator FR performance while also allowing for a comparison between human and automated FR performance.


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