Analyzing Covariate Influence on Gender and Race Prediction From Near-Infrared Ocular Images.

Recent research has explored the possibility of automatically deducing information, such as gender, age, and race, of an individual from their biometric data. While the face modality has been extensively studied in this regard, the iris modality less so. In this paper, we first review the medical literature to establish a biological basis for extracting gender and race cues from the iris. Then, we demonstrate that it is possible to use simple texture descriptors, such as binarized statistical image feature and local binary patterns, to extract gender and race attributes from a near-infrared ocular image used in a typical iris recognition system. The proposed method predicts gender and race from a single eye image with an accuracy of 86% and 90%, respectively. In addition, the following analysis is conducted: 1) the role of different parts of the ocular region on attribute prediction; 2) the influence of gender on race prediction and vice versa; 3) the impact of eye color on gender and race prediction; 4) the impact of image blur on gender and race prediction; 5) the generalizability of the method across different datasets; and 6) the consistency of prediction performance across the left and right eyes.