Three-dimensional non-parametric method for limbus detection

Purpose To present a novel non-parametric algorithm for detecting the position of the human eye limbus in three dimensions and a new dynamic method for measuring the full 360° visible iris boundary known as white-to-white distance along the eye horizontal line. Methods The study included 88 participants aged 23 to 65 years (37.7±9.7), 47 females and 41 males. Clinical characteristics, height data and the apex coordinates and 1024×1280 pixel digital images of the eyes were taken by an Eye Surface Profiler and processed by custom-built MATLAB codes. A dynamic light intensity frequency based white-to-white detection process and a novel three-dimensional method for limbus detection is presented. Results Evidence of significant differences (p<0.001) between nasal-temporal and superior-inferior white-to-white distances in both right and left eyes were found (nasal-temporal direction; 11.74±0.42 mm in right eyes and 11.82±0.47 mm in left eyes & superior-inferior direction; 11.52±0.45 mm in right eyes and 11.55±0.46 mm in left eyes). Average limbus nasal-temporal diameters were 13.64±0.55 mm for right eyes, and 13.74±0.40 mm for left eyes, however the superior-inferior diameters were 13.65±0.54 mm, 13.75±0.38 mm for right and left eyes, respectively. No significant difference in limbus contours has been observed either between the nasal-temporal direction (p = 0.91) and the superior-inferior direction (p = 0.83) or between the right (p = 0.18) and left eyes (p = 0.16). Evidence of tilt towards the nasal-temporal side in the three-dimensional shape of the limbus was found. The right eyes mean limbus contour tilt around the X-axis was -0.3±1.35° however, their mean limbus contour tilt around the Y-axis was 1.76±0.9°. Likewise, the left eyes mean limbus contour tilt around the X-axis was 0.77±1.25° and the mean limbus contour tilt around the Y-axis was -1.54±0.89°. Conclusions The white-to-white distance in the human eye is significantly larger in the nasal-temporal direction than in the superior-inferior direction. The human limbus diameter was found not to vary significantly in these directions. The 3D measures show that the limbus contour does not lay in one plane and tends to be higher on the nasal-inferior side of the eye

A solution based on iris recognition for data protection on mobile devices

Orientadores: Leandro Aparecido Villas, Fabio Augusto FariaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O uso de dispositivos móveis em atividades diárias, tais como, acessar a serviços bancários, efetuar compras on-line e trocar mensagens via e-mail ou redes sociais, tornou-se mais frequente na vida das pessoas. Assegurar a privacidade e a segurança dos dados contidos nesses dispositivos, é essencial para incentivar o surgimento de novas aplicações e aumentar a adesão de novos usuários. Para este fim, a utilização de biometria tem sido muito atraente, devido o alto grau de dissimilaridade entre as propriedades biológicas de indivíduos distintos. Em particular, a biometria por meio de íris destaca-se dentre as demais técnicas de biometria, por possuir um padrão de textura rico em detalhes que permanecem estáveis ao longo da vida, diferentemente da impressão digital e da face que estão sujeitas a grandes variações. Além disso, não há necessidade de hardware adicional, uma vez que o sensor presente na maioria dos dispositivos móveis é suficiente para capturar as imagens de íris a distância, sem haver contato direto do indivíduo com o sensor (menos intrusiva). Contudo, lidar com imagens de íris capturadas por usuários ingênuos (proprietários de dispositivos móveis) e em ambientes não controlados torna o reconhecimento de íris uma tarefa desafiadora, e por isso, requer métodos mais robustos para desempenhar essa tarefa. Este trabalho apresenta uma solução baseada em descritores binários de pontos-chave (ou keypoints) para realizar a codificação da textura de íris, onde apenas os pixels entorno dos keypoints são utilizados na codificação, por considerar que alguns pixels são mais estáveis do que outros, e desta forma, é reduzido o impacto causado pela fragilidade de bits, problema frequente em abordagens que utilizam todos os pixels para gerar a codificação. São comparados três descritores binários de keypoints bem conhecidos (BRIEF, ORB e BRISK) para identificar qual é o mais adequado para ser utilizado na codificação da textura de íris. Em comum, os três descritores são eficientes no uso de memória e no tempo de processamento, e possuem bom desempenho para aplicações de reconhecimento. Além disso, são comparados três diferentes métodos de segmentação existentes na literatura para avaliar o impacto causado no desempenho do reconhecimento. São utilizados dois conjuntos de imagens de íris amplamente conhecidos na literatura, chamados CASIA e MICHE-I, para avaliação da solução proposta. Os resultados das simulações apresentaram um bom desempenho, considerando as métricas de eficácia, uso de memória e tempo de processamentoAbstract: The use of mobile devices in daily activities, such as access to banking services, online buying, send/receive emails or interaction on social networks, has become more often on people¿s lives. To ensure the privacy and security of the data contained on these devices it is essential to encourage the emergence of new applications and increase users involvement. To this end, biometrics has been very attractive due to the high dissimilarity among biological properties of distinct individuals. In particular, iris recognition stands out among other biometric techniques. The iris presents a rich texture pattern that remains stable throughout our life. On the other hand, other biometric techniques are subject to large variations. Furthermore, no additional hardware is required, since that sensor already present in most mobile devices is sufficient to capture the iris image at distance, without any direct contact with the sensor (less intrusive). However, dealing with samples captured by naive users (owners of mobile devices) and uncontrolled settings make iris recognition a challenging task, and therefore requires more robust methods to address them. In this work we present a solution based on binary keypoint descriptors to perform iris encoding. In our solution, only surrounding pixels of keypoints are used, considering that some pixels are more stable than others, and thus, the impact caused by bit fragility is reduced, which is a frequent problem in approaches that use all the pixels to generate the coding. Three well known binary keypoint descriptors - BRIEF, ORB and BRISK - are compared to identify which is the most suitable for use on the iris encoding. In common, all of them are efficient and have good performance for recognition applications. Moreover, three different segmentation methods are compared to assess the impact on recognition performance. We used two iris datasets widely known in literature, called CASIA and MICHE-I, to assess the proposed solution. The simulation results shown a good performance considering the metrics effectiveness, memory usage and processing timeMestradoCiência da ComputaçãoMestre em Ciência da Computação4716.4Funcam

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Iris Segmentation Using Pupil Location, Linearization, and Limbus Boundary Reconstruction in Ambient Intelligent Environments

Advances in sensors and technology, on one side, and recognition techniques, on the other side, make the iris a top candidate for biometric use. Iris detection and segmentation, however, are still lacking. We propose here a novel iris segmentation technique using pupil location, linearization, and limbus boundary reconstruction, and show its feasibility and comparative advantages against existing methods. © 2010 Springer-Verlag

Iris segmentation using pupil location, linearization, and limbus boundary reconstruction in ambient intelligent environments

Advances in sensors and technology, on one side, and recognition techniques, on the other side, make the iris a top candidate for biometric use. Iris detection and segmentation, however, are still lacking. We propose here a novel iris segmentation technique using pupil location, linearization, and limbus boundary reconstruction, and show its feasibility and comparative advantages against existing methods