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IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART B: CYBERNETICS, VOL. 37, NO. 5, OCTOBER 2007 1149 Fusing Face-Verification Algorithms and Humans
 

Summary: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS--PART B: CYBERNETICS, VOL. 37, NO. 5, OCTOBER 2007 1149
Fusing Face-Verification Algorithms and Humans
Alice J. O'Toole, Hervé Abdi, Fang Jiang, and P. Jonathon Phillips, Senior Member, IEEE
Abstract--It has been demonstrated recently that state-of-the-
art face-recognition algorithms can surpass human accuracy at
matching faces over changes in illumination. The ranking of
algorithms and humans by accuracy, however, does not provide
information about whether algorithms and humans perform the
task comparably or whether algorithms and humans can be fused
to improve performance. In this paper, we fused humans and
algorithms using partial least square regression (PLSR). In the
first experiment, we applied PLSR to face-pair similarity scores
generated by seven algorithms participating in the Face Recogni-
tion Grand Challenge. The PLSR produced an optimal weighting
of the similarity scores, which we tested for generality with a jack-
knife procedure. Fusing the algorithms' similarity scores using
the optimal weights produced a twofold reduction of error rate
over the most accurate algorithm. Next, human-subject-generated
similarity scores were added to the PLSR analysis. Fusing humans
and algorithms increased the performance to near-perfect classi-

  

Source: Abdi, Hervé - School of Behavioral and Brain Sciences, University of Texas at Dallas

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences