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

Abstract

Soft biometric information such as gender can contribute to many applications like as identification and security. This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors. It uses the same pipeline for iris recognition systems consisting of iris segmentation, normalisation and then classification. Experiments show that applying BSIF is not straightforward since it can create artificial textures causing misclassification. In order to overcome this limitation, a new set of filters was trained from eye images and different sized filters with padding bands were tested on a subject-disjoint database. A Modified-BSIF (MBSIF) method was implemented. The latter achieved better gender classification results (94.6% and 91.33% for the left and right eye respectively). These results are competitive with the state of the art in gender classification. In an additional contribution, a novel gender labelled database was created and it will be available upon request.

Authentication through gender classification from iris images using support vector machine

“…They concluded that combining the best aspects of the iris‐code from the left and right eyes resulted in an 89% reliable gender predictor. Later, Tapia and Arellano (2019) proposed a modified‐binary statistical features (MBSIF) method for gender classification and attained 94.6 and 91.33% for the left and right eye, respectively.…”