Synthetic Periocular Iris PAI from a Small Set of Near-Infrared-Images

Biometric has been increasing in relevance these days since it can be used for several applications such as access control for instance. Unfortunately, with the increased deployment of biometric applications, we observe an increase of attacks. Therefore, algorithms to detect such attacks (Presentation Attack Detection (PAD)) have been increasing in relevance. The LivDet-2020 competition which focuses on Presentation Attacks Detection (PAD) algorithms have shown still open problems, specially for unknown attacks scenarios. In order to improve the robustness of biometric systems, it is crucial to improve PAD methods. This can be achieved by augmenting the number of presentation attack instruments (PAI) and bona fide images that are used to train such algorithms. Unfortunately, the capture and creation of presentation attack instruments and even the capture of bona fide images is sometimes complex to achieve. This paper proposes a novel PAI synthetically created (SPI-PAI) using four state-of-the-art GAN algorithms (cGAN, WGAN, WGAN-GP, and StyleGAN2) and a small set of periocular NIR images. A benchmark between GAN algorithms is performed using the Frechet Inception Distance (FID) between the generated images and the original images used for training. The best PAD algorithm reported by the LivDet-2020 competition was tested for us using the synthetic PAI which was obtained with the StyleGAN2 algorithm. Surprisingly, The PAD algorithm was not able to detect the synthetic images as a Presentation Attack, categorizing all of them as bona fide. Such results demonstrated the feasibility of synthetic images to fool presentation attacks detection algorithms and the need for such algorithms to be constantly updated and trained with a larger number of images and PAI scenarios.

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

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.

Sex-Classification from Cell-Phones Periocular Iris Images

Selfie soft biometrics has great potential for various applications ranging from marketing, security and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach that increases the resolution of low quality periocular iris images cropped from selfie images of subject's faces. This work shows that increasing image resolution (2x and 3x) can improve the sex-classification rate when using a Random Forest classifier. The best sex-classification rate was 90.15% for the right and 87.15% for the left eye. This was achieved when images were upscaled from 150x150 to 450x450 pixels. These results compare well with the state of the art and show that when improving image resolution with the SRCNN the sex-classification rate increases. Additionally, a novel selfie database captured from 150 subjects with an iPhone X was created (available upon request).