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Soft biometric information such as gender can contribute to many applicationslike as identification and security. This paper explores the use of a BinaryStatistical Features (BSIF) algorithm for classifying gender from iris textureimages captured with NIR sensors. It uses the same pipeline for irisrecognition systems consisting of iris segmentation, normalisation and thenclassification. Experiments show that applying BSIF is not straightforwardsince it can create artificial textures causing misclassification. In order toovercome this limitation, a new set of filters was trained from eye images anddifferent sized filters with padding bands were tested on a subject-disjointdatabase. A Modified-BSIF (MBSIF) method was implemented. The latter achievedbetter gender classification results (94.6\% and 91.33\% for the left and righteye respectively). These results are competitive with the state of the art ingender classification. In an additional contribution, a novel gender labelleddatabase was created and it will be available upon request.

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