25-08-2017, 09:32 PM
automated eye -pattern recognition systems
[attachment=41549]
Abstract
A biometric system provides automatic identification of an individual
based on a unique feature or characteristic possessed by the individual. Iris recognition is
regarded as the most reliable and accurate biometric identification system available. Most
commercial iris recognition systems use patented algorithms developed by Daugman, and
these algorithms are able to produce perfect recognition rates. However, published results
have usually been produced under favorable conditions, and there have been no independent trials
of the technology.
The work presented in this thesis involved developing an ‘open-source’
iris recognition system in order to verify both the uniqueness of the human iris and also its
performance as a biometric. For determining the recognition performance of the system two
databases of digitized grayscale eye images were used.
The iris recognition system consists of an automatic segmentation system that is
based on the Hough transform, and is able to localize the circular iris and pupil region,
occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized
into a rectangular block with constant dimensions to account for imaging inconsistencies.
Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels
to encode the unique pattern of the iris into a bit-wise biometric template.
Introduction:
Automated Eye-Pattern Recognition Systems
Iris recognition is an automated method of biometric identification that uses
mathematical pattern-recognition techniques on video images of the irises of an individual's eyes,
whose complex random patterns are unique and can be seen from some distance.
Not to be confused with another, less prevalent, ocular-based technology,
retina scanning, and iris recognition uses camera technology with subtle infrared illumination to
acquire images of the detail-rich, intricate structures of the iris. Digital templates encoded from
these patterns by mathematical and statistical algorithms allow unambiguous positive
identification of an individual. Databases of enrolled templates are searched by matcher engines
at speeds measured in the millions of templates per second per (single-core) CPU, and with
infinitesimally small False Match rates.
Many millions of persons in several countries around the world have been
enrolled in iris recognition systems, for convenience purposes such as passport-free automated
border-crossings, and some national ID systems based on this technology are being deployed. A
key advantage of iris recognition, besides its speed of matching and its extreme resistance to
False Matches is the stability of the iris as an internal, protected, yet externally visible organ of
the eye.
The majority of iris recognition cameras use Near Infrared (NIR) imaging by
emitting 750nm wavelength low-power light. This is done because dark-brown eyes, possessed
by the majority of the human population, reveal rich structure in the NIR but much less in the
visible band (400 - 700nm), and also because NIR light is invisible and unobtrusive. A further
important reason is that by allowing only this selected narrow band of illuminating light back
into the camera via its filters, most of the ambient corneal reflections from a bright environment
are blocked from contaminating the iris patterns.
Literature Survey
The core algorithms that underlie iris recognition were developed in the
1990's by Professor John Daugman, PhD, OBE (University of Cambridge Computer
Laboratory). These were licensed to many developers of commercial iris cameras and systems
including LG Electronics, Oki, Panasonic, Sagem, Iris Guard, and Sarnoff Labs.
As of 2008, Daugman's algorithms are the basis of all commercially
deployed iris recognition systems, although many alternative approaches have been studied and
compared in the academic literature in hundreds of publications. Iris recognition remains a very
active research topic in computing, engineering, statistics, and applied mathematics.
Theory
Iris recognition is an automated method of biometric identification that
uses mathematical pattern-recognition techniques on video images of the irises of an individual's
eyes, whose complex random patterns are unique and can be seen from some distance.
The melanin, also known as chromospheres, mainly consists of two distinct
heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–
reddish). NIR imaging is not sensitive to these chromospheres, and as a result they do not appear
in the captured images.
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.
[attachment=41549]
Abstract
A biometric system provides automatic identification of an individual
based on a unique feature or characteristic possessed by the individual. Iris recognition is
regarded as the most reliable and accurate biometric identification system available. Most
commercial iris recognition systems use patented algorithms developed by Daugman, and
these algorithms are able to produce perfect recognition rates. However, published results
have usually been produced under favorable conditions, and there have been no independent trials
of the technology.
The work presented in this thesis involved developing an ‘open-source’
iris recognition system in order to verify both the uniqueness of the human iris and also its
performance as a biometric. For determining the recognition performance of the system two
databases of digitized grayscale eye images were used.
The iris recognition system consists of an automatic segmentation system that is
based on the Hough transform, and is able to localize the circular iris and pupil region,
occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized
into a rectangular block with constant dimensions to account for imaging inconsistencies.
Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels
to encode the unique pattern of the iris into a bit-wise biometric template.
Introduction:
Automated Eye-Pattern Recognition Systems
Iris recognition is an automated method of biometric identification that uses
mathematical pattern-recognition techniques on video images of the irises of an individual's eyes,
whose complex random patterns are unique and can be seen from some distance.
Not to be confused with another, less prevalent, ocular-based technology,
retina scanning, and iris recognition uses camera technology with subtle infrared illumination to
acquire images of the detail-rich, intricate structures of the iris. Digital templates encoded from
these patterns by mathematical and statistical algorithms allow unambiguous positive
identification of an individual. Databases of enrolled templates are searched by matcher engines
at speeds measured in the millions of templates per second per (single-core) CPU, and with
infinitesimally small False Match rates.
Many millions of persons in several countries around the world have been
enrolled in iris recognition systems, for convenience purposes such as passport-free automated
border-crossings, and some national ID systems based on this technology are being deployed. A
key advantage of iris recognition, besides its speed of matching and its extreme resistance to
False Matches is the stability of the iris as an internal, protected, yet externally visible organ of
the eye.
The majority of iris recognition cameras use Near Infrared (NIR) imaging by
emitting 750nm wavelength low-power light. This is done because dark-brown eyes, possessed
by the majority of the human population, reveal rich structure in the NIR but much less in the
visible band (400 - 700nm), and also because NIR light is invisible and unobtrusive. A further
important reason is that by allowing only this selected narrow band of illuminating light back
into the camera via its filters, most of the ambient corneal reflections from a bright environment
are blocked from contaminating the iris patterns.
Literature Survey
The core algorithms that underlie iris recognition were developed in the
1990's by Professor John Daugman, PhD, OBE (University of Cambridge Computer
Laboratory). These were licensed to many developers of commercial iris cameras and systems
including LG Electronics, Oki, Panasonic, Sagem, Iris Guard, and Sarnoff Labs.
As of 2008, Daugman's algorithms are the basis of all commercially
deployed iris recognition systems, although many alternative approaches have been studied and
compared in the academic literature in hundreds of publications. Iris recognition remains a very
active research topic in computing, engineering, statistics, and applied mathematics.
Theory
Iris recognition is an automated method of biometric identification that
uses mathematical pattern-recognition techniques on video images of the irises of an individual's
eyes, whose complex random patterns are unique and can be seen from some distance.
The melanin, also known as chromospheres, mainly consists of two distinct
heterogeneous macromolecules, called eumelanin (brown–black) and pheomelanin (yellow–
reddish). NIR imaging is not sensitive to these chromospheres, and as a result they do not appear
in the captured images.
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.