VeriEye Algorithm Features and Capabilities

Performance numbers are provided for a PC with Intel Core 2 Q9400 processor (2.67 GHz).

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Figure 2
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Neurotechnology began research and development in the field of eye iris biometrics in 1994. In 2008, Neurotechnology released VeriEye iris recognition algorithm. The next year VeriEye was recognized by NIST as one of the most reliably accurate iris recognition algorithms.

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

• Robust iris detection. Irises are detected even when the images have obstructions, visual noise and different levels of illumination. Lighting reflections, eyelids and eyelashes obstructions are eliminated. Images with narrowed eyelids or eyes that are gazing away are also accepted.
• Automatic interlacing detection and correction. The correction results in maximum quality of iris features templates from moving iris images.
• Gazing-away eyes. A gazing-away iris image is correctly detected, segmented and transformed as if it were looking directly into the camera (see Figure 1).
• Correct iris segmentation is achieved under these conditions:
  • Perfect circles fail. VeriEye uses active shape models that more precisely model the contours of the eye, as iris boundaries are not modeled by perfect circles.
  • The centers of the iris inner and outer boundaries are different (see Figure 2). The iris inner boundary and its center are marked in red, the iris outer boundary and its center are marked in green.
  • Iris boundaries are definitely not circles and even not ellipses (see Figure 3) and especially in gazing-away iris images.
  • Iris boundaries seem to be perfect circles. The recognition quality can still be improved if boundaries are found more precisely (see Figure 4). Note these slight imperfections when compared to perfect circular white contours.
• Fast matching. Configurable matching speed varies from 60,000 to 600,000 comparisons per second. See technical specifications for more details.
• Reliability. VeriEye 2.5 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 testing results for more details.

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).

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Technical Specifications

64 pixels is the minimal radius of circle containing full iris texture, that is required for iris template extraction.

Near-infrared spectral region is recommended for iris image capture.

All iris templates should be loaded into RAM before identification, thus the maximum iris template database size is limited by the amount of available RAM.

VeriEye iris template matching algorithm can be run on more than one processor core on multi-core processors allowing to increase template matching speed. The template matching speeds in the table below are given as a range, where the smaller number means matching speed using 1 processor core, while the larger number means matching speed using all 4 processor cores. The specifications are provided for these processors:

• Intel Core 2 Q9400 (4 cores), running at 2.67 GHz clock rate;
• Intel Core i7-2600 (4 cores), running at 3.4 GHz clock rate.

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Reliability and Performance Test Results

We present the testing results to show how VeriEye 2.5 technical specifications correspond the practical algorithm's performance and reliability evaluations. Iris images from several standard databases were used for testing, thus the testing results can be compared with testing results of other algorithms. All databases contained iris images with 640 x 480 pixels size.

(1) The full IRISDB1600 database contains 31,997 images (image size 1280x960 pixels), representing 799 unique persons and 1,598 unique irises. A subset used in this test was preprocessed similar to NIST IREX experiments:
    (a) Images were downsampled to 640x480 via 2x2 neighborhood averaging.
    (b) All images containing irises with diameters larger than 340 pixels were removed.

Two tests were performed with each database:

• Test 1 maximized matching accuracy. VeriEye 2.5 algorithm reliability in this test is shown as blue curves on the ROC charts.
• Test 2 maximized matching speed. VeriEye 2.5 algorithm reliability in this test is shown as red curves on the ROC charts.

The iris rotation tolerance was set to ±15° in all tests.

Receiver operation characteristics (ROC) curves are usually used to demonstrate the recognition quality of an algorithm. ROC curves show the dependence of false rejection rate (FRR) on the false acceptance rate (FAR).

ICE2005 Exp1 Click to zoom

ND-IRIS-0405 Click to zoom

Bath IRISDB1600 Click to zoom

Template matching was performed using all 4 cores of the specified processors. The performance tests were performed on PCs with these processors:

• Intel Core 2 Q9400, running at 2.67 GHz clock rate;
• Intel Core i7-2600, running at 3.4 GHz clock rate.

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