Adaptive Quality-Based Performance Prediction and Boosting for Iris


Feb 22, 2014 (4 years and 4 months ago)


Adaptive Quality-Based Performance Prediction and Boosting for Iris
Authentication: Methodology and Its Illustration
In this paper we are studying one of the biometric authentication process i.e., Iris
authentication which is used to provide security in a specific way.
Image Processing, Security
Popular biometric approaches with physiological characters like DNA Matching, Ear,Eyes -
Retina Recognition, Face Recognition, Fingerprint Recognition,Finger Geometry
Recognition, Gait,Hand Geometry Recognition, Voice - Speaker Verification /
Authentication, Voice - Speaker Identification, Signature Recognition,Vein Recognition,
Eyes - Iris Recognition.Among these approaches, the iris has some advantages over the
others and has received a lot of attention in the last two decades.Iris recognition is an
automated method of biometric identification that uses mathematical pattern-recognition
techniques on images of the iris of an individual's eyes,whose complex random patterns are
unique and can be seen from some distance.The human iris, an annular region located around
the pupil and covered by the cornea, can provide independent and unique information of a
person. Furthermore, the iris is highly stable with age, and it is difficult to fake the iris under
the protection of the cornea.
An eye image contains not only the iris texture but also some irrelevant
parts.The pupillary and limbic boundaries should be detected to isolate the annular iris
region. Several iris segmentation methods for non ideal iris images have been proposed.The
previous iris segmentation consists of two stages. First the gradient image around the iris
boundaries in the radial direction from the pupil to the sclera is generated.Generally,the pupil
is darker than the iris, and the iris is darker than the sclera.In this proposed system we have
presented three new methods for the performance of the biometric recognition (iris) system.
The basic idea beyond this project is to processing the images of iris to provide
Fingerprint recognition or fingerprint authentication refers to theautomated method of
verifying a match between two human fingerprints.Fingerprints are one of many forms of
biometrics used to identify an individual and verify their identity. This article touches on two
major classes of algorithms (minutia and pattern)and four sensor designs (optical, ultrasonic,
passive capacitance, and active capacitance).Illumination should not be visible or bright in
existing system.
 Small target (1 cm) to acquire from a distance (1 m)
 Moving target ...within another... on yet another
 Located behind a curved, wet, reflecting surface
 Obscured by eyelashes, lenses, reflections
 Partially occluded by eyelids, often drooping
Biometric-based personal verification and identification methods have gained much interest
with an increasing emphasis on security.Iris recognition is a fast, accurate and secure
biometric technique that can operate in both verification and identification modes.The iris
texture pattern has no links with the genetic structure of an individual and since it is
generated by chaotic processes .Externally visible; patterns imaged from a distance Iris
patterns possess a high degree of randomness.
 In proposed system we are using three new methods to improve performance of a
single biometric matcher.
 uniqueness: set by combinatorial complexity.
 variability: 244 degrees-of-freedom.
 entropy: 3.2 bits per square-millimeter.
 Highly protected, internal organ of the eye.
 Java, Swing
 Main Processor : > 2GHz
 Ram : 1 GB
 Hard Disk : 80GB
1.Security for all industries
2.Biometric security process (EYE)