Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks

The iris can be considered as one of the most important biometric traits due to its high degree of uniqueness. Iris-based biometrics applications depend mainly on the iris segmentation whose suitability is not robust for different environments such as near-infrared (NIR) and visible (VIS) ones. In this paper, two approaches for robust iris segmentation based on Fully Convolutional Networks (FCNs) and Generative Adversarial Networks (GANs) are described. Similar to a common convolutional network, but without the fully connected layers (i.e., the classification layers), an FCN employs at its end a combination of pooling layers from different convolutional layers. Based on the game theory, a GAN is designed as two networks competing with each other to generate the best segmentation. The proposed segmentation networks achieved promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4, IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in both non-cooperative and cooperative domains, outperforming the baselines techniques which are the best ones found so far in the literature, i.e., a new state of the art for these datasets. Furthermore, we manually labeled 2,431 images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks available for research purposes.Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 201

Improving less constrained iris recognition

The iris has been one of the most reliable biometric traits for automatic human authentication due to its highly stable and distinctive patterns. Traditional iris recognition algorithms have achieved remarkable performance in strictly constrained environments, with the subject standing still and with the iris captured at a close distance. This enables the wide deployment of iris recognition systems in applications such as border control and access control. However, in less constrained environments with the subject at-a-distance and on-the-move, the iris recognition performance is significantly deteriorated, since such environments induce noise and degradations in iris captures. This restricts the applicability and practicality of iris recognition technology for some real-world applications with more open capturing conditions, such as surveillance, forensic and mobile device security applications. Therefore, robust algorithms for less constrained iris recognition are desirable for the wider deployment of iris recognition systems. This thesis focuses on improving less constrained iris recognition. Five methods are proposed to improve the performance of different stages in less constrained iris recognition. First, a robust iris segmentation algorithm is developed using l1-norm regression and model selection. This algorithm formulates iris segmentation as robust l1-norm regression problems. To further enhance the robustness, multiple segmentation results are produced by applying l1-norm regression to different models, and a model selection technique is used to select the most reliable result. Second, an iris liveness detection method using regional features is investigated. This method seeks not only low level features, but also high level feature distributions for more accurate and robust iris liveness detection. Third, a signal-level information fusion algorithm is presented to mitigate the noise in less constrained iris captures. With multiple noisy iris captures, this algorithm proposes a sparse-error low rank matrix factorization model to separate noiseless iris structures and noise. The noiseless structures are preserved and emphasised during the fusion process, while the noise is suppressed, in order to obtain more reliable signals for recognition. Fourth, a method to generate optimal iris codes is proposed. This method considers iris code generation from the perspective of optimization. It formulates traditional iris code generation method as an optimization problem; an additional objective term modelling the spatial correlations in iris codes is applied to this optimization problem to produce more effective iris codes. Fifth, an iris weight map method is studied for robust iris matching. This method considers both intra-class bit stability and inter-class bit discriminability in iris codes. It emphasises highly stable and discriminative bits for iris matching, enhancing the robustness of iris matching. Comprehensive experimental analysis are performed on benchmark datasets for each of the above methods. The results indicate that the presented methods are effective for less constrained iris recognition, generally improving state-of-the-art performance

Métricas de qualidade aplicadas em sistemas de reconhecimento de íris

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2019.A qualidade de imagens de íris em sistemas de reconhecimento de íris influencia direta- mente na etapa de segmentação, assim como a qualidade da segmentação afeta a extração dos atributos que identificam unicamente uma pessoa. Este projeto propõe, então, o es- tudo da aplicação de duas métricas de qualidade, uma para avaliar a qualidade de imagens de íris de comprimento de luz visível e outra para avaliar a qualidade da etapa de seg- mentação de íris, em sistemas de reconhecimento de íris. Uma arquitetura de sistemas de reconhecimento de íris é proposta com as métricas Índice de Sinal-Magnitude Diferencial (DSMI) e Avaliação da Correlação de Atributos (FCE). Os experimentos são realizados utilizando um sistema de reconhecimento de íris open source e quatro bancos de imagens de íris de luz visível. A arquitetura proposta melhorou consideravelmente o desempenho de sistemas de reconhecimento de íris nos dois bancos com imagens mais comportadas e melhorou pouco ou piorou o desempenho dos outros dois bancos. No entanto, os dois resultados adversos estão mais relacionados com o banco de imagens do que com a ar- quitetura proposta, de forma que pode-se afirmar que a arquitetura tem o potencial de ser utilizada em sistemas de reconhecimento de íris.Iris images quality in iris recognition systems are strongly related to the segmentation step success, as the segmentation quality in the iris feature extraction step that identifies uniquely a person. This project consists of a study on the application of two quality metrics, one for measuring the quality of iris images in the visible light wavelength and the other for measuring the iris segmentation step quality in iris recognition systems. An iris recognition framework is proposed with the quality metrics Differential Sign-Magnitude Statistics Index (DSMI) and Feature Correlation Evaluation (FCE). The experiments were performed using an open source iris recognition system and four visible light wavelength iris image datasets. The proposed framework improved considerably the performance of iris recognition systems in two of the datasets with images captured in a more controlled environment and improved a little or got worse the performance in the other two datasets. However, the two unfavorable results are more related to the datasets than the proposed framework, so it can be affirmed that the framework has the potential for being applied in iris recognition systems

E-mentoring as a platform for the development of novice teacher competencies at a rural school in the Western Cape

Thesis (MEd (Education)--Cape Peninsula University of Technology, 2019The focus of this study was to investigate the potential of an e-platform to mentor novice teachers in a rural school in the Western Cape. The premise of this study was that the Western Cape Education Department had no formal e-mentoring programmes in its basket of CPD programmes that specifically addresses the professional isolation of rural-based novice teachers. The problem was investigated by means of a literature review and empirical investigation, using a mixed method approach. The study had an explicit qualitative slant with a minor quantitative input. The findings of the study reveal that novice teachers’ initial competencies and skills can be improved through e-platform support. Novice teachers operate in an era of social media immersion and are willing to embrace virtually based support through social media. The study recommends that social media has the potential to build rural based novice teachers’ competencies and skills, as a standalone exercise or part of a blended learning experience. Furthermore, in time, e-mentoring could make a meaningful contribution to the development of rural based teachers, whether novice or experienced

Biometrics based privacy-preserving authentication and mobile template protection

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others