To obtain a quantitative assessment of the state of automatic face recognition, we performed a meta-analysis of performance results of face recognition algorithms in the literature. The analysis was conducted on 24 papers that report identification performance on frontal facial images and used either the FERET or ORL database in their experiments. The analysis shows that control scores are predictive of performance of novel algorithms at statistically significant levels. The analysis identified three methodological areas for improvement in automatic face recognition. First, the majority of papers report experimental results for face recognition problems that are already solved. Second, authors do not adequately document their experiments. Third, performance results for novel or experimental algorithms need to be accompanied by control algorithm performance scores.