19-02-2011, 04:04 PM
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Automated Eye-Pattern Recognition Systems
What is Iris?
• The colored part of the eye is called the iris.
• It controls light levels inside the eye.
• The iris is embedded with tiny muscles that dilate and constrict the pupil size.
• The iris is flat and divides the front of the eye from the back of the eye.
• Its color comes from microscopic pigment cells called melanin.
Eye Diagram
Characteristics of Iris
• Has highly distinguishing texture.
• Right eye differs from left eye.
• Twins have different iris texture.
• Iris pattern remains unchanged after the age of two and does not degrade overtime or with the environment.
• Iris patterns are extremely complex than other biometric patterns.5
History of Eye-Pattern Recognition System
• In the mid-1980s, ophthalmologists Leonard Flom and Aran Safir realized that no two patient’s irises were alike.
• In 1987, the pair were issued the so-called Flom patent, which has given the company they founded, Iridian Technologies, dominance in the iris-recognition market. 6
Need of Eye-Pattern Recognition Technology
• Illusion for Data Privacy.
• Passwords, or Social Security Numbers can be cracked easily.
• Eye pattern recognition system virtually eliminates fake authentication and identity privacy and safely controls authorized entry to sensitive sites, data or material.
• Iris Pattern is most distinguished than any other facial feature and do not change overtime and research show the matching accuracy of iris recognition systems is greater than that of DNA testing.
• Iris recognition system is easy to operate, comfortable and is virtually impossible to deceive.
• Since the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a living password that one need not remember but one always carries. .
Operating Principle
• An iris-recognition algorithm first has to identify the approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye.
• The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information that is essential for a statistically meaningful comparison between two iris images.
• The mathematical methods used resemble those of modern lossy compression algorithms for photographic images.
• In the case of Daugman's algorithms, a Gabor wavelet transform is used in order to extract the spatial frequency range that contains a good signal-tonoise ratio considering the focus quality of available cameras.
• The result is a set of complex numbers that carry local amplitude and phase information for the iris image.
• In Daugman's algorithms, all amplitude information is discarded, and the resulting 2048 bits that represent an iris consist only of the complex sign bits of the Gabor-domain representation of the iris image.
• Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination and virtually negligibly by iris color, which contributes significantly to the long-term stability of the biometric template.
• To authenticate via identification or verification , a template created by imaging the iris is compared to a stored value template in a database.
• If the Hamming distance is below the decision threshold, a positive identification has effectively been made.
• A practical problem of iris recognition is that the iris is usually partially covered by eyelids and eyelashes.
• In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eyelids and eyelashes and to exclude the bits in the resulting code from the comparison operation.
Automated Eye-Pattern Recognition Systems
What is Iris?
• The colored part of the eye is called the iris.
• It controls light levels inside the eye.
• The iris is embedded with tiny muscles that dilate and constrict the pupil size.
• The iris is flat and divides the front of the eye from the back of the eye.
• Its color comes from microscopic pigment cells called melanin.
Eye Diagram
Characteristics of Iris
• Has highly distinguishing texture.
• Right eye differs from left eye.
• Twins have different iris texture.
• Iris pattern remains unchanged after the age of two and does not degrade overtime or with the environment.
• Iris patterns are extremely complex than other biometric patterns.5
History of Eye-Pattern Recognition System
• In the mid-1980s, ophthalmologists Leonard Flom and Aran Safir realized that no two patient’s irises were alike.
• In 1987, the pair were issued the so-called Flom patent, which has given the company they founded, Iridian Technologies, dominance in the iris-recognition market. 6
Need of Eye-Pattern Recognition Technology
• Illusion for Data Privacy.
• Passwords, or Social Security Numbers can be cracked easily.
• Eye pattern recognition system virtually eliminates fake authentication and identity privacy and safely controls authorized entry to sensitive sites, data or material.
• Iris Pattern is most distinguished than any other facial feature and do not change overtime and research show the matching accuracy of iris recognition systems is greater than that of DNA testing.
• Iris recognition system is easy to operate, comfortable and is virtually impossible to deceive.
• Since the iris is a protected internal organ whose random texture is stable throughout life, it can serve as a living password that one need not remember but one always carries. .
Operating Principle
• An iris-recognition algorithm first has to identify the approximately concentric circular outer boundaries of the iris and the pupil in a photo of an eye.
• The set of pixels covering only the iris is then transformed into a bit pattern that preserves the information that is essential for a statistically meaningful comparison between two iris images.
• The mathematical methods used resemble those of modern lossy compression algorithms for photographic images.
• In the case of Daugman's algorithms, a Gabor wavelet transform is used in order to extract the spatial frequency range that contains a good signal-tonoise ratio considering the focus quality of available cameras.
• The result is a set of complex numbers that carry local amplitude and phase information for the iris image.
• In Daugman's algorithms, all amplitude information is discarded, and the resulting 2048 bits that represent an iris consist only of the complex sign bits of the Gabor-domain representation of the iris image.
• Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination and virtually negligibly by iris color, which contributes significantly to the long-term stability of the biometric template.
• To authenticate via identification or verification , a template created by imaging the iris is compared to a stored value template in a database.
• If the Hamming distance is below the decision threshold, a positive identification has effectively been made.
• A practical problem of iris recognition is that the iris is usually partially covered by eyelids and eyelashes.
• In order to reduce the false-reject risk in such cases, additional algorithms are needed to identify the locations of eyelids and eyelashes and to exclude the bits in the resulting code from the comparison operation.
