Relevant features for gender classification in NIR periocular images

Abstract

Most gender classifications methods from near-infrared (NIR) images have used iris information. Recent work has explored the use of the whole periocular iris region which has surprisingly achieved better results. This suggests the most relevant information for gender classification is not located in the iris as expected. In this work, the authors analyse and demonstrate the location of the most relevant features that describe gender in periocular NIR images and evaluate their influence in classification. Experiments show that the periocular region contains more gender information than the iris region. They extracted several features (intensity, texture, and shape) and classified them according to their relevance using the XgBoost algorithm. Support vector machine and nine ensemble classifiers were used for testing gender accuracy when using the most relevant features. The best classification results were obtained when 4000 features located on the periocular region were used (89.22%). Additional experiments with the full periocular iris images versus the iris-occluded images were performed. The gender classification rates obtained were 84.35 and 85.75%, respectively. From results, they suggest focusing only on the surrounding area of the iris. This allows us to realise a faster classification of gender from NIR periocular images.