How features of the human face affect recognition: a statistical comparison of three face recognition algorithms

Recognition difficulty is statistically linked to 11 subject covariate factors such as age and gender for three face recognition algorithms: principle components analysis, an interpersonal image difference classifier, and an elastic bunch graph matching algorithm. The covariates assess race, gender, age, glasses use, facial hair, bangs, mouth state, complexion, state of eyes, makeup use, and facial expression. We use two statistical models. First, an ANOVA relates covariates to normalized similarity scores. Second, logistic regression relates subject covariates to probability of rank one recognition. These models have strong explanatory power as measured by R/sup 2/ and deviance reduction, while providing complementary and corroborative results. Some factors, like changes to the eye status, affect all algorithms similarly. Other factors, such as race, affect different algorithms differently. Tabular and graphical summaries of results provide a wealth of empirical evidence. Plausible explanations of many results can be motivated from knowledge of the algorithms. Other results are surprising and suggest a need for further study.