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_1: Previous researchers have reported success in predicting ethnicity and in predicting gender from features of the iris texture. This paper is the first to consider both problems using similar experimental approaches. Contributions of this work include greater accuracy than previous work on predicting ethnicity from iris texture, empirical evidence that suggests that gender prediction is harder than ethnicity prediction, and empirical evidence that ethnicity prediction is more difficult for females than for males.
  @cite_11: This paper is concerned in analyzing iris texture in order to determine “soft biometric”, attributes of a person, rather than identity. In particular, this paper is concerned with predicting the gender of a person based on analysis of features of the iris texture. Previous researchers have explored various approaches for predicting the gender of a person based on iris texture. We explore using different implementations of Local Binary Patterns from the iris image using the masked information. Uniform LBP with concatenated histograms significantly improves accuracy of gender prediction relative to using the whole iris image. Using a subject-disjoint test set, we are able to achieve over 91 correct gender prediction using the texture of the iris. To our knowledge, this is the highest accuracy yet achieved for predicting gender from iris texture.
summary: Later, @cite_1 used a Support Vector Machine (SVM) classifier with features extracted using spot and line detectors and Law's texture measures. They used a dataset of 600 images and a cross-validation protocol with 2, 5 and 10 folds, with person-disjoint partitions. They considered both race-from-iris and gender-from-iris, and their classification accuracy on gender-from-iris ranged from @math 83 In the work of @cite_11 , using an SVM to classify Local Binary Pattern (LBP) features extracted from 3,000 iris images yielded an accuracy of @math 80 20 @math train test @math 72 28 @math 60