Neurotechnology began research and development in the field of eye iris biometrics in 1994. In 2008, Neurotechnology released a PC-based iris recognition algorithm, VeriEye 2.0, that is designed for biometrical system integrators. The proprietary algorithm features:

• Reliability. VeriEye 2.0 algorithm shows excellent performance when tested on all publicly available datasets. Especially good results are achieved on the recent NIST ICE2005 Exp1 database with iris images of intentionally degraded quality (see section below ).

• Speed. VeriEye 2.0 iris enrollment time is less than 0.5 sec. and matching speed is configurable 50,000-150,000 irises per second in 1:N identification mode. To confirm these results with your samples, please try VeriEye algorithm demo application (see section below ).

• Uniqueness. The new proprietary iris recognition algorithm is based on original methods that solve the drawbacks and limitations of existing state-of-the-art algorithms.

• Robustness. Eye irises are detected even when the images have obstructions, visual noise and different levels of illumination. Images with narrowed eyelids or eyes that are gazing away are also accepted.

• Simple multi-biometric system integration. Compatibility with fingerprint and facial identification technologies from the same vendor allows the VeriEye algorithm to be used together with other Neurotechnology biometrical algorithms.

• Flexible licensing and pricing. VeriEye is offered for a competitive price . Developers can select from several types of SDK and licensing models. Each of these kits and models is intended for specific needs, and developers always can make an upgrade by paying the difference between the current and more powerful SDK.

Algorithm

The VeriEye 2.0 iris recognition algorithm implements advanced iris segmentation, enrollment and matching using robust digital image processing algorithms:

• Iris boundaries are not modeled by perfect circles. VeriEye uses active shape models that more precisely model the contours of the eye, resulting in correct iris segmentation when perfect circles fail.

• Correct segmentation is achieved even when the centers of the iris inner and outer boundaries are different (see Figure 1). The iris inner boundary and its center are marked in red, the iris outer boundary and its center are marked in green.

• Correct segmentation when iris boundaries are definitely not circles and even not ellipses (see Figure 2) and especially in gazing-away iris images.

• Even when iris boundaries seem to be perfect circles, recognition quality can still be improved if boundaries are found more precisely (see Figure 3). Note these slight imperfections when compared to perfect circular white contours.

• Automatic interlacing detection and correction results in maximum quality of iris features templates from moving iris images.

• Elimination of lighting reflections, eyelids and eyelashes obstructions.

• Detection and correction of gazing-away iris images (see Figure 4). A gazing-away eye is correctly segmented and transformed as if it were looking directly into the camera.

• Configurable matching speed varies from 50,000 to 150,000 comparisons per second. The highest speed still preserves nearly the same recognition quality (see Figure 5).

All performance evaluations were determined for one core of Intel Core 2 Duo running at 2.66 GHz.

All iris images are taken from CASIA Iris Image Database V2.0 and CASIA Iris Image Database V3.0 collected by the Chinese Academy of Sciences Institute of Automation (CASIA) (http://www.cbsr.ia.ac.cn/english/IrisDatabases.asp).

Reliability Tests and Technical Specifications

VeriEye 2.0 was tested with iris images from several standard databases, thus the testing results can be compared with testing results of other algorithms. Usually the algorithm recognition quality is expressed by receiver operation characteristics (ROC) curves that show the dependence of false rejection rate on the false acceptance rate. The presented ROC curves show the results of testing VeriEye 2.0 with iris images from these databases:

• CASIA Iris Image Databases V1.0 and V3.0 (interval) (see Figure 6);

• CASIA Iris Image Databases V2.0 (device1) (see Figure 7);

• ICE2005 Exp1 iris image database (see Figure 8).