CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)

Abstract

Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images. Considering the importance of this research area and its commercial applications, it is highly essential for researchers to make use of efficient color features in their algorithms which necessitates the production of color iris image databases. The present study introduces an iris image database for gender classification and proposes a new gender classification algorithm for its evaluation. The database consists of iris images taken from 720 subjects including 370 females and 350 males in university students. For each student, more than 6 images were taken from his/her both left and right eyes. After examining the images, 3 images from the left eye and 3 images from the right eye were selected among the most appropriate images and were included in the database. All 4320 images from this database were taken under the same condition and by the same color camera. Finally, the quality and the efficiency of the introduced database are evaluated using a new method that extract Zernike moments on spectral features and two well-known classifiers, namely, SVM and KNN. The results revealed that there is a significant improvement in gender classification compared with the similar databases.