Face recognition algorithms surpass humans matching faces over changes in illumination.
There has been significant progress in improving the performance of computer-based face recognition algorithms over the last decade. Although algorithms have been tested and compared extensively with each other, there has been remarkably little work comparing the accuracy of computer-based face recognition systems with humans. We compared seven state-of-the-art face recognition algorithms with humans on a facematching task. Humans and algorithms determined whether pairs of face images, taken under different illumination conditions, were pictures of the same person or of different people. Three algorithms surpassed human performance matching face pairs prescreened to be "difficult" and six algorithms surpassed humans on "easy" face pairs. Although illumination variation continues to challenge face recognition algorithms, current algorithms compete favorably with humans. The superior performance of the best algorithms over humans, in light of the absolute performance levels of the algorithms, underscores the need to compare algorithms with the best current control--humans.
O'Toole
Alice J
AJ
School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX 75083, USA. otoole@utdallas.edu
Jonathon Phillips
P
P
Jiang
Fang
F
Ayyad
Janet
J
Penard
Nils
N
Abdi
Hervé
H
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008029:Lighting / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique