2017/02/04 by Andrey Kuehlkamp, Kuehlkamp, Andrey, Benedict Becker +4 · 4 citations
Arts and Humanities · Computer Science · Mathematics · #Artificial intelligence #Biometric Identification and Security #Biometrics #Channel (broadcasting) #Computer science #Convolutional neural network #Disjoint sets #Eyelash #Face recognition and analysis #Forensic Anthropology and Bioarchaeology Studies #IRIS (biosensor) #Imperfect #Iris recognition #Machine learning #Mathematics #Pattern recognition (psychology) #Segmentation #cs.CV
paper · pdf · doi:10.48550/arxiv.1702.01304
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/02/04 · openalex publication_date 2017/02/04 · arxiv updated 2017/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.