Gender-from-Iris or Gender-from-Mascara?
By Andrey Kuehlkamp, Benedict Becker, Kevin W. Bowyer. 2017
35 citations recorded by the source corpus.
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.
Subfields: Archeology, Computer Vision and Pattern Recognition, Signal Processing
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Archeology
In Computer Vision and Pattern Recognition
- Christina Heinze-Deml
- Benjamin S. Glicksberg
- Joel T. Dudley
- Nicolai Meinshausen
- Marcus A. Badgeley
- John R. Zech
- Bernhard Schölkopf
In Signal Processing
Citations in the corpus
- Deep learning predicts hip fracture using confounding patient and healthcare variables (2018, background): “Combining an image-only model result with other clinical data improves upon the clinical data alone, but does not reach the performance of directly modeling all data (Supplementary Tables 15 – 16).”
- Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (2017, result): “uch domain shifts can be caused by changing conditions such as color, background or location changes. Predictive performance is then likely to degrade. For example, consider the analysis presented in Kuehlkamp et al. (2017) which is concerned with the problem of predicting a person’s gender based on”