02-03-2011, 11:47 AM
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INTRODUCTION
1.1ABSTRACT:
Iris recognition is the process of recognizing a person by analyzing the apparent pattern of his or her iris. There is a strong scientific demand for the proliferation of systems, concepts and algorithms for iris recognition and identification. This is mostly because of the comparatively short time that iris recognition systems have been around. In comparison to face, fingerprint and other biometric traits there is still a great need for substantial mathematical and computer-vision research and insight into iris recognition. One evidence for this is the total lack of publicly available adequate datasets of iris images.
The program converts a photo of an eye to an 'unrolled' depiction of the subject's iris and matches the eye to the agent's memory. If a match is found, it outputs a best match. The current functionality matches that proposed in the original requirements.
1.2 PROJECT DESCRIPTION:
Iris recognition is the most powerful biometric technology there is. Nothing else comes close.
• Most accurate
• Scalable
• Opt-in
• Non-contact
• Interoperable cameras
The following 8 stages occur in our project to be implemented:
8 Stages of Iris Detection:
Stage 1: Scan Eye
The eye scanning will be simulated in this system as we have no method of taking real-time images of subjects. Therefore, all eye images are to be jpeg image files at least 1000 x 1000 pixels in dimension. The eye is scanned by manually selecting the file and instructing the agent to scan it. The agent begins scanning an eye by turning the jpeg file into an image object in full color (24 bit RGB).
Stage 2: Eye Gray scaling Algorithm
The agent next converts the full-color image to an 8-bit representation. This reduces space complexity, making further computations faster without losing reliability.
Stage 3: Median Filter
INTRODUCTION
1.1ABSTRACT:
Iris recognition is the process of recognizing a person by analyzing the apparent pattern of his or her iris. There is a strong scientific demand for the proliferation of systems, concepts and algorithms for iris recognition and identification. This is mostly because of the comparatively short time that iris recognition systems have been around. In comparison to face, fingerprint and other biometric traits there is still a great need for substantial mathematical and computer-vision research and insight into iris recognition. One evidence for this is the total lack of publicly available adequate datasets of iris images.
The program converts a photo of an eye to an 'unrolled' depiction of the subject's iris and matches the eye to the agent's memory. If a match is found, it outputs a best match. The current functionality matches that proposed in the original requirements.
1.2 PROJECT DESCRIPTION:
Iris recognition is the most powerful biometric technology there is. Nothing else comes close.
• Most accurate
• Scalable
• Opt-in
• Non-contact
• Interoperable cameras
The following 8 stages occur in our project to be implemented:
8 Stages of Iris Detection:
Stage 1: Scan Eye
The eye scanning will be simulated in this system as we have no method of taking real-time images of subjects. Therefore, all eye images are to be jpeg image files at least 1000 x 1000 pixels in dimension. The eye is scanned by manually selecting the file and instructing the agent to scan it. The agent begins scanning an eye by turning the jpeg file into an image object in full color (24 bit RGB).
Stage 2: Eye Gray scaling Algorithm
The agent next converts the full-color image to an 8-bit representation. This reduces space complexity, making further computations faster without losing reliability.
Stage 3: Median Filter
