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Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation. [ABSTRACT FROM AUTHOR]

The LivDet‐2020 competition focuses on Presentation Attacks Detection (PAD) algorithms, has still open problems, mainly unknown attack scenarios. It is crucial to enhance PAD methods. This can be achieved by augmenting the number of Presentation Attack Instruments (PAI) and Bona fide (genuine) images used to train such algorithms. Unfortunately, the capture and creation of PAI and even the capture of Bona fide images are sometimes complex to achieve. The generation of synthetic images with Generative Adversarial Networks (GAN) algorithms may help and has shown significant improvements in recent years. This paper presents a benchmark of GAN methods to achieve a novel synthetic PAI from a small set of periocular near‐infrared images. The best PAI was obtained using StyleGAN2, and it was tested using the best PAD algorithm from the LivDet‐2020. The synthetic PAI was able to fool such an algorithm. As a result, all images were classified as Bona fide. A MobileNetV2 was trained using the synthetic PAI as a new class to achieve a more robust PAD. The resulting PAD was able to classify 96.7% of synthetic images as attacks. BPCER10 was 0.24%. Such results demonstrated the need for PAD algorithms to be constantly updated and trained with synthetic images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]