Iris image quality assessment based on ISO/IEC 29794-6:2015 standard / Avaliação da qualidade da imagem da íris com base na norma ISO / IEC 29794-6: 2015

O processo de reconhecimento biométrico da íris é uma das tecnologias biométricas mais consistentes entre outras disponíveis atualmente. No entanto, sua eficiência e precisão podem ser afetadas por imagens de íris de baixa qualidade usadas como entrada para um sistema de reconhecimento, assim, o desempenho global é reduzido. Nesse contexto, este trabalho propõe um estudo de avaliação para determinar o impacto da qualidade da imagem da íris no desempenho do sistema biométrico da íris, utilizando as principais métricas apresentadas na norma ISO / IEC 29794-6: 2015. Os testes experimentais são realizados usando um banco de dados de imagens de íris e o software de reconhecimento biométrico OSIRIS, ambos amplamente aceitos e referenciados nas últimas pesquisas. Os resultados experimentais mostram os valores de intervalo de cada métrica de qualidade e o número de imagens que atingem os valores mínimos necessários. O desempenho do sistema biométrico é avaliado pelos parâmetros True-Match (TM) e False Non-Match (FNM); assim, foi possível identificar que quanto maior o nível de qualidade da imagem, menor o valor de FNM; portanto, o desempenho do sistema é aprimorado. 

Iris recognition method based on segmentation

The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studie

The Viterbi algorithm at different resolutions for enhanced iris segmentation

International audienceWe propose in this paper a novel method for iris segmentation. In order to retrieve iris contours, the Viterbi algorithm is applied on the gradient map of images processed by Anisotropic Smoothing. The Viterbi algorithm is exploited at two resolutions: at a high resolution, it allows finding precise contours, while at a low resolution, coarse contours that improve the accuracy of normalization circles are retrieved. We tested the method on several databases (ICE 2005, NDIRIS-0405, Casia-V3-Interval). Our extensive experiments on such databases lead to performance at the state of the art, while the proposed method does not require a refined parameter adaptation to the various degradations encountered