  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 periocular 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
discriminative power of the iris texture for gender is weaker than previously
thought, and that the gender-related information is primarily in the periocular
region.
