Computer vision & A.I.

VeriEye Algorithm Features and Capabilities

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 there are obstructions to the image, visual noise and/or 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 results in maximum quality of iris features templates from moving iris images.
  • Gazing-away eyes are correctly detected on images, segmented and transformed as if it were looking directly into the camera (see Figure 1).
  • Correct iris segmentation is obtained even 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.
  • Reliability. VeriEye 2.10 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) (

AFIS or multi-biometric fingerprint, iris, face and voice identification for large-scale systems.

Face identification for PC, mobile and Web solutions.

Fingerprint identification for PC, mobile and Web solutions.

Iris identification for PC, mobile and Web solutions.

Speaker recognition for PC, mobile and Web applications.

Ready-to-use robotics development kit.

More products for developers:

End-user products:
  • NCheck Bio Attendance – an attendance control application that uses fingerprint or face biometrics to perform persons identification.
  • NVeiler Video Filter – a plug-in for VirtualDub that automatically detects faces in a frame, tracks the faces (or other objects) in subsequent frames and hides them.
  • NPointer – a freeware application for gesture- and voice-based computer control that captures hand movements with a webcam and accepts voice commands.
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