Abstract
This paper presents a complete iris identification system including three main stages: iris segmentation, signature extraction, and signature comparison. An accurate and robust pupil and iris segmentation process, taking into account eyelid occlusions, is first detailed and evaluated. Then, an original wavelet-packet-based signature extraction method and a novel identification approach, based on the fusion of local distance measures, are proposed. Performance measurements validating the proposed iris signature and demonstrating the benefit of our local-based signature comparison are provided. Moreover, an exhaustive evaluation of robustness, with regards to the acquisition conditions, attests the high performances and the reliability of our system. Tests have been conducted on two different databases, the well-known CASIA database (V3) and our ISEP database. Finally, a comparison of the performances of our system with the published ones is given and discussed.
Highlights
Biometric systems provide reliable automatic recognition of persons based on one or several biological features
In the following we propose a signature extraction method which uses the energy of the Wavelet packets (WP) coefficients as discriminator for determining which subbands carry the most useful part of the information
These experiments show that our biometric signature and identification process, applied on accurately segmented images, lead to a zero error identification system, which is very robust to acquisition conditions, in terms of illumination variability, focusing, and optical axis deviation
Summary
Biometric systems provide reliable automatic recognition (identification) of persons based on one or several biological features. Even though Daugman’s system is the most successful and the most well known, many other approaches have been proposed Such recognition systems, in spite of their specificities, have the EURASIP Journal on Advances in Signal Processing same structure: the first stage consists in the iris segmentation, the image is normalized and features are extracted in order to generate a signature. This signature is compared to reference signatures (i.e., gallery database) in order to measure a numerical dissimilarity value to be used in the decision process.
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