Gender-from-Iris or Gender-from-Mascara?

Abstract

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 genderfrom-iris texture than has been suggested in previous work.

Predicting Gender From Iris Texture May Be Harder Than It Seems

“…At the same time, performing classification exclusively on the eyebrow images yielded accuracies from 80-95%. Although this work seems to have been conducted independently and not focused on iris, its results seem to confirm findings of [2,3] and [8], suggesting a majority of gender cues could be located around the eye.…”

“…In [16], SVM classification using the IrisCode as features was reported to achieve 85% accuracy, on disjoint train and test partitions. On the other hand, [8] takes a more attentive look at evaluation methodology, and finds out that reporting accuracy from a single random train/test split may give rise to biased results. Their accuracy, averaged over 10 repetitions on random disjoint train/test partitions was 66%.…”

“…These works have used several different methods and protocols, and reported quite different results. In our previous work [8] we addressed some issues that may have been overlooked by earlier works. There, it is shown that the presence of eye cosmetics and the use of less rigorous cross-validation protocols may have resulted in optimistic estimates of classification accuracy.…”

Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features

“…Some research has utilised uniform patterns or combined uniform patterns with non-uniform patterns to improve per-formance [38,27]. A small number of methods have used Deep Learning on Soft-biometrics such as gender with periocular NIR images [18,34,28].…”

Sex-Prediction from Periocular Images Across Multiple Sensors and Spectra

“…Such soft-biometric features within the periocular region are vital for robust sex-prediction. Nevertheless, it is important to note that subjects can take influence on said features and classification performance is expected to drop if these are absent [16].…”