Analyzing the Impact of Gender Misclassification on Face Recognition Accuracy

Abstract

Automated face recognition technologies have been under scrutiny in recent years due to noted variations in accuracy relative to race and gender. Much of this concern was driven by media coverage of high error rates for women and persons of color reported in an evaluation of commercial gender classification ('gender from face”) tools. Many decried the conflation of errors observed in the task of gender classification with the task of face recognition. This motivated the question of whether images that are misclas-sified by a gender classification algorithm have increased error rate with face recognition algorithms. In the first experiment, we analyze the False Match Rate (FMR) of face recognition for comparisons in which one or both of the images are gender-misclassified. In the second experiment, we examine match scores of gender-misclassified images when compared to images from their labeled versus classified gender. We find that, in general, gender misclassified images are not associated with an increased FMR. For females, non-mated comparisons involving one misclassified image actually shift the resultant impostor distribution to lower similarity scores, representing improved accuracy. To our knowledge, this is the first work to analyze (1) the FMR of one- and two-misclassification error pairs and (2) non-mated match scores for misclassified images against labeled- and classified-gender categories.