Predicting Gender From Iris Texture May Be Harder Than It Seems

Andrey Kuehlkamp, Kevin Bowyer

Abstract:

Predicting gender from iris images has been reported by
several researchers as an application of machine learning
in biometrics. Recent works on this topic have suggested
that the preponderance of the gender cues is located in the
periocular region rather than in the iris texture itself. This
paper focuses on teasing out whether the information for
gender prediction is in the texture of the iris stroma, the pe-
riocular region, or both. We present a larger dataset for
gender from iris, and evaluate gender prediction accuracy
using linear SVM and CNN, comparing hand-crafted and
deep features. We use probabilistic occlusion masking to
gain insight on the problem. Results suggest the discrimi-
native power of the iris texture for gender is weaker than
previously thought, and that the gender-related information
is primarily in the periocular region.