text: Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.
  @cite_10: Prediction of gender characteristics from iris images has been investigated and some successful results have been reported in the literature, but without considering performance for different iris features and classifiers. This paper investigates for the first time an approach to gender prediction from iris images using different types of features (including a small number of very simple geometric features, texture features and a combination of geometric and texture features) and a more versatile and intelligent classifier structure. Our proposed approaches can achieve gender prediction accuracies of up to 90 in the BioSecure Database.
summary: An overview of the techniques and results used so far is presented in Table . None of these works has looked systematically at the effect of cosmetics on accuracy of predicting gender-from-iris. Most of the works do not use a subject-disjoint training and testing, especially those reporting the highest accuracy. And these works report accuracy from a single random split into train-test data, rather than a mean of N random splits. Apart from @cite_10 , no other research employed neural networks for this task.