Saturday, August 8, 2009
Labels: Biomatric
Introduction
Iris recognition is the process of recognizing a person by analyzing the random pattern of the iris (Figure 1). 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 (Figure 2).
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.
Approach
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 Iris Codes® (Figures 4 & 5).
Iris patterns are described by an Iris Code 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 Iris Codes are compared. The amount of difference between two Iris Codes — Hamming Distance (HD) — is used as a test of statistical independence between the two Iris Codes. If the HD indicates that less than one-third of the bytes in the Iris Codes are different, the Iris Code fails the test of statistical significance, indicating that the Iris Codes are from the same iris. Therefore, the key concept to iris recognition is failure of the test of statistical independence.
Iris vs. Retina Recognition
As discussed above, iris recognition utilizes the iris muscle to perform verification. Retinal recognition uses the unique pattern of blood vessels on an individual’s retina at the back of the eye. The figure below illustrates the structure of the eye
Both techniques involve capturing a high quality picture of the iris or retina, using a digital camera. In the acquisition of these images, some form of illumination is necessary. Both techniques use NIR (near infrared) light. Although safe in a properly designed system, eye safety is a major concern for all systems that illuminate the eye. Because infrared has insufficient energy to cause photochemical effects, the principal potential damage modality is thermal. When NIR is produced using light emitting diodes, the resulting light is incoherent. Any risk for eye safety is remote with a single LED source using today's LED technology. Multiple LED illuminators can, however, produce eye damage if not carefully designed and used.
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