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.
Gender is one soft biometric attribute, and gender recognition has been explored using biometric traits such as faces, fingerprints, gait and irises. The earliest work on gender-from-iris @cite_12 used a classifier based on decision trees, and reported an accuracy of about 75 extracted hand-crafted geometric and texture features from log-Gabor-filtered images in a dataset of over 57,000 images. The training and testing sets were not person-disjoint, which typically results in a higher estimated accuracy than can be expected for new persons.
@cite_12
2623203442
Gender is an important demographic attribute. Automated gender estimation has drawn significant interest in numerous applications such as surveillance, human-computer interaction, anonymous customized advertisement systems, image retrieval system as well as in fusion with primary biometric trait to improve the matching accuracy of a biometric system. Although studies have been proposed for gender estimation from face, fingerprint and iris images, none of the existing studies explore gender prediction from ocular images captured in mobile environment, which is the aim of this paper. We evaluate several textural descriptors in combination with Support Vector Machines (SVM) and Multi-layer Perceptron (MLP) on a large scale publicly available VISOB dataset captured using mobile devices. The best accuracy of 91.6 is obtained using Histogram of Gradient (HOG) descriptor in combination with MLP classifier.