Explainer: Iris Recognition

June 23, 2012 - 

Iris recognition is the process of recognizing a person by analyzing the random pattern of the iris. The automated method of iris recognition is relatively young, existing in patent only since 1994.

The iris is a muscle within the eye that regulates the size of the pupil, controlling the amount of light that enters the eye. It is the colored portion of the eye with coloring based on the amount of melatonin pigment within the muscle.

Although the coloration and structure of the iris is genetically linked, the details of the patterns are not. The iris develops during prenatal growth through a process of tight forming and folding of the tissue membrane. Prior to birth, degeneration occurs, resulting in the pupil opening and the random, unique patterns of the iris. Although genetically identical, an individual’s irides are unique and structurally distinct, which allows for it to be used for recognition purposes.

In 1936, ophthalmologist Frank Burch proposed the concept of using iris patterns as a method to recognize an individual. In 1985, Drs. Leonard Flam and Aran Safir, ophthalmologists, proposed the concept that no two irides are alike, and were awarded a patent for the iris identification concept in 1987. Dr. Flom approached Dr. John Daugman to develop an algorithm to automate identification of the human iris. In 1993, the Defense Nuclear Agency in the United States began work to test and deliver a prototype unit, which was successfully completed by 1995 due to the combined efforts of Drs. Flom, Safir, and Daugman. In 1994, Dr. Daugman was awarded a patent for his automated iris recognition algorithms. In 1995, the first commercial products became available. In 2005, the broad patent covering the basic concept of iris recognition expired, providing marketing opportunities for other companies that have developed their own algorithms for iris recognition. The patent on the “lrisCodes” implementation of iris recognition developed by Dr. Daugman expired in 2011.

Before recognition of the iris takes place, the iris is located using landmark features. These landmark features and the distinct shape of the iris allow for imaging, feature isolation, and extraction. Localization of the iris is an important step in iris recognition because, if done improperly, resultant noise (e.g., eyelashes, reflections, pupils, and eyelids) in the image may lead to poor performance.

Iris imaging requires use of a high quality digital camera. Today’s commercial iris cameras typically use infrared light to illuminate the iris without causing harm or discomfort to the subject. Upon imaging an iris, a 2D Gabor wavelet filters and maps the segments of the iris into phasors (vectors). These phasors include information on the orientation and spatial frequency (“what” of the image) and the position of these areas (“where” of the image). This information is used to map the lrisCodes.

Iris patterns are described by an lrisCode using phase information collected in the phasors. The phase is not affected by contrast, camera gain, or illumination levels. The phase characteristic of an iris can be described using 256 bytes of data using a polar coordinate system. Also included in the description of the iris are control bytes that are used to exclude eyelashes, reflection(s), and other unwanted data.

To perform the recognition, two lrisCodes are compared. The amount of difference between two lrisCodes – Hamming Distance (HD) – is used as a test of statistical independence between the two lrisCodes. If the HD indicates that less than one-third of the bytes in the lrisCodes are different, the lrisCode fails the test of statistical significance, indicating that the lrisCodes are from the same iris. Therefore, the key concept to iris recognition is failure of the test of statistical independence.

Source: National Science and Technology Council (NSTC)

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About Stephen Mayhew

Stephen Mayhew is the publisher and co-founder of Biometrics Research Group, Inc.. His experience includes a mix of entrepreneurship, brand development and publishing. Stephen attended Carleton University and lives in Toronto, Canada. Connect with Stephen on LinkindIn.