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.
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.
@cite_10
2584117724 2320355672 2052294358 2406143150
When analysing human activities using data mining or machine learning techniques, it can be useful to infer properties such as the gender or age of the people involved. This paper focuses on the sub-problem of gender recognition, which has been studied extensively in the literature, with two main problems remaining unsolved: how to improve the accuracy on real-world face images, and how to generalise the models to perform well on new datasets. We address these problems by collecting five million weakly labelled face images, and performing three different experiments, investigating: the performance difference between convolutional neural networks (CNNs) of differing depths and a support vector machine approach using local binary pattern features on the same training data, the effect of contextual information on classification accuracy, and the ability of convolutional neural networks and large amounts of training data to generalise to cross-database classification. We report record-breaking results on both the Labeled Faces in the Wild (LFW) dataset, achieving an accuracy of 98.90 , and the Images of Groups (GROUPS) dataset, achieving an accuracy of 91.34 for cross-database gender classification. Previous researchers have explored various approaches for predicting the gender of a person based on the features of the iris texture. This paper is the first to predict gender directly from the same binary iris code that could be used for recognition. We found that the information for gender prediction is distributed across the iris, rather than localized in particular concentric bands. We also found that using selected features representing a subset of the iris region achieves better accuracy than using features representing the whole iris region. We used the measures of mutual information to guide the selection of bits from the iris code to use as features in gender prediction. Using this approach, with a person-disjoint training and testing evaluation, we were able to achieve 89 correct gender prediction using the fusion of the best features of iris code from the left and right eyes. 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. 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.