title: Eye-catching eyebrows: training and evaluating humans for periocular image verification.
abstract: High-quality periocular images have sufficient variability between people that they can be used for automated recognition. However, there are no standards for training people to compare periocular images in a forensic context. Potential methods for training people to compare eyebrow images are investigated in this article. Pairs of images were presented to volunteers who decided whether the images showed the same person or different people. A control group with minimal instruction and no training responded with 87.6% accuracy. A second group viewed 100 training pairs before the test and achieved 94.2% accuracy. A third group rated specific features in the images and had 0.1% improvement over the second group. Experimental results show that actively comparing at least thirty training images and receiving immediate feedback improves trainees' performance six to seven percent. If a quantitative quality score is needed, a linear combination of similarity ratings on different features may be used.
authors: Department of Computer Science and Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN 46556.
authors: Karen P
authors: KP
authors: Hollingsworth
mesh_terms: D056667:Biometric Identification / Q000379:methods*; D016022:Case-Control Studies; D005138:Eyebrows / Q000033:anatomy & histology*; D005145:Face / Q000033:anatomy & histology; D005246:Feedback; D044707:Forensic Sciences / Q000193:education; D006197:Hair; D006801:Humans; D008027:Light; D012867:Skin / Q000033:anatomy & histology; D012984:Software; D019265:Spectroscopy, Near-Infrared
keywords: biometrics; eyebrow; face recognition; forensic science; near-infrared light; ocular; periocular