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Comparing Human and Automatic Face Recognition Performance
 

Summary: Comparing Human and Automatic Face
Recognition Performance
Andy Adler
Systems and Computer Engineering,
Carleton University, Ottawa, Canada
adler@site.uOttawa.ca
Michael E. Schuckers
Mathematics, Computer Science and Statistics Department,
St. Lawrence University, Canton, NY, USA and
Center for Identification Technology Research (CITeR)
West Virginia University, Morgantown, WV, USA
schuckers@stlawu.edu
Abstract
Face recognition technologies have seen dramatic improvements in performance over the past decade, and such
systems are now widely used for security and commercial applications. Since recognizing faces is a task that humans
are understood to be very good at, it is common to want to compare automatic (AFR) and human (HFR) face
recognition in terms of biometric performance. This paper addresses this question by: 1) conducting verification tests
on volunteers (HFR) and commercial AFR systems, and 2) developing statistical methods to support comparison of
the performance of different biometric systems. HFR was tested by presenting face image pairs and asking subjects
to classify them on a scale of "Same", "Probably Same", "Not sure", "Probably Different", and "Different"; the

  

Source: Adler, Andy - Department of Systems and Computer Engineering, Carleton University

 

Collections: Computer Technologies and Information Sciences