Repeated Measures GLMM Estimation of Subject-Related and False Positive Threshold Effects on Human Face Verification Performance

Subject covariate data were collected on 1, 072 pairs of FERET images for analysis in a human face verification experiment. The subject data included information about facial hair, bangs, eyes, gender, and age. The verification experiment was replicated at seven different false alarm rates ranging from 1/10, 000 to 1/100. A generalized linear mixed model (GLMM) was fit to the binary outcomes indicating correct verification. Statistically significant main effects for bangs, eyes, gender, and age were found. The effect of the log false positive rate on verification success was found to interact significantly with bangs, gender, and age. These results have important implications for future evaluation of biometrics, and the GLMM methodology used here is shown to be effective and informative for this sort of data.

[1]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System , 2005, Machine Vision and Applications.

[2]  Bruce A. Draper,et al.  Using a Generalized Linear Mixed Model to Study the Configuration Space of a PCA+LDA Human Face Recognition Algorithm , 2004, AMDO.

[3]  A. O'Toole,et al.  Simulating the ‘Other-race Effect* as a Problem in Perceptual Learning , 1991 .

[4]  Sandor Z. Der,et al.  FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results. , 1996 .

[5]  James P. Egan,et al.  Signal detection theory and ROC analysis , 1975 .

[6]  J. van Leeuwen,et al.  Audio- and Video-Based Biometric Person Authentication , 2001, Lecture Notes in Computer Science.

[7]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Bruce A. Draper,et al.  How features of the human face affect recognition: a statistical comparison of three face recognition algorithms , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  Mark Von Tress,et al.  Generalized, Linear, and Mixed Models , 2003, Technometrics.

[10]  Douglas A. Reynolds,et al.  SHEEP, GOATS, LAMBS and WOLVES A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation , 1998 .

[11]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure , 2003, ICVS.

[12]  J. Melo,et al.  Overview and summary , 1985 .

[13]  Hyeonjoon Moon,et al.  A verification protocol and statistical performance analysis for face recognition algorithms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[14]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[15]  R. Wolfinger,et al.  Generalized linear mixed models a pseudo-likelihood approach , 1993 .

[16]  P. Jonathon Phillips,et al.  Face Recognition Vendor Test 2002 Performance Metrics , 2003, AVBPA.