Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features
This work addresses gender classification for biometric applications like identification and security, presenting an incremental improvement over existing methods.
The paper tackled gender classification from iris texture images by developing a Modified-BSIF method, which achieved 94.6% and 91.33% accuracy for left and right eyes, respectively, overcoming issues with artificial textures in the original BSIF algorithm.
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.