Identification par analyse en paquets d'ondelettes de l'iris et tests de robustesse

Cet article présente un système de reconnaissance par l'iris, fondé sur l'analyse en paquets d'ondelettes orthogonales. Une mesure d'énergie permet de choisir les paquets qui extraient l'information discriminante sur la texture de l'iris. Les tests, conduits sur une base de 149 images de très bonne qualité, provenant de 69 yeux différents, montrent une bonne robustesse aux conditions de prises de vues, notamment aux variations d'éclairement, au flou, à une déviation de l'axe optique, et à des défauts locaux dans l'image

Enhanced iris recognition: Algorithms for segmentation, matching and synthesis

This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique

Reconhecimento biométrico considerando a deformação não linear da íris humana

The biometric systems that use the information on iris texture has received great attention in recent years. The extraordinary variation in iris texture allows the creation of recognition and identification systems with almost zero error rates. However, in general, researches ignore the problems associated with contraction and dilation iris movements that can result in significant differences between the enrollment images and the probe image. This work, in addition to developing a traditional iris recognition system, comprising the steps of detection, segmentation, normalization, encoding and comparison, determines quantitatively the iris motion effect in recognition system accuracy. In addition, this paper proposes a new method to reduce the influence of dynamic iris, verified by decidability and the Equal Error Rate (EER), obtained in the comparison between iris codes in very different expansion states. The new method uses Dynamic Time Warping technique to correct and compare the gradient vectors extracted from iris texture. Thus, the most discriminant features of the test image and enrollment image are aligned and compared, considering the non-linear distortion of the iris tissue. Experimental results using dynamic images indicate that system performance gets worse with comparison on images in different states contraction. For direct comparison with contracted and dilated iris the proposed method improves the decidability of 3.50 to 4.39 and EER of 9.69% to 3.36%.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo a Pesquisa do Estado de Minas GeraisTese (Doutorado)Os sistemas biométricos que utilizam a informação contida na textura da íris têm recebido grande atenção nos últimos anos. A grande variação em textura da íris permite o desenvolvimento de sistemas de reconhecimento e de identificação com taxas de erro quase nulas. Entretanto, de forma geral, as pesquisas nesta área ignoram os problemas associados aos movimentos de contração e dilatação da íris que geram diferenças significativas entre as imagens inscritas em uma base de dados e a imagem de teste. Este trabalho, além de desenvolver um sistema de reconhecimento de íris tradicional, composto pelas etapas de detecção, segmentação, normalização, codificação e comparação, determina de forma quantitativa o efeito dos movimentos da íris na precisão do sistema de reconhecimento. Além disso, este trabalho propõe um novo método para diminuir a influência da dinâmica da íris, verificado pela decidibilidade e pela Taxa de Erro Igual (EER), obtidas na comparação entre códigos de íris em estados de dilatação bem diferentes. O novo método utiliza a técnica Dynamic Time Warping para corrigir e comparar os vetores de gradientes extraídos da textura da íris. Dessa forma, as características mais discriminantes da imagem de teste e da imagem da galeria são alinhadas e comparadas, considerando a deformação não linear do tecido da íris. Os resultados experimentais, utilizando imagens dinâmicas, indicam que a performance do sistema piora quando a comparação é feita com imagens em estados de contração diferentes. Para a comparação direta entre íris bem contraída com íris bem dilatada o método proposto melhora a decidibilidade de 3,50 para 4,39 e a EER de 9,69% para 3,36%