Bio-metric identification gave us a powerful technique to provide identity and became a worth in the authentication of identity and access command termed as Iris recognition.
Iris recognition is an example of a well-judged bio-metric identification along with system, with accurate results in the areas of a security system. In this technology, our system presents a more precise method which is popularly called as RANSAC which stands for Random Sample Consensus.
It is usually for fitting an ellipse around the non-circular boundaries of iris. It is an act of using striking/visible and near-infrared ray to take a high-contrast picture of an iris. It is typically a form of a bio-metric technology that comes under a same category such as fingerprinting and facial recognition. Iris scanning elevates the notable civil liberties, and also the privacy concerns. The vastly developing technologies make it possible to scan irises from a distance, which indicates that data could be collected fraudulently, without letting anyone know about it, let alone permit. There are several security troubles as well that are:
If a database containing bio-metric information is lost or compromised, it is nearly impossible to have a new set of eyes as one will have a reissued number of credit-card. Iris bio-metrics are generally collected and thereby stored by any third-party vendor which vastly expands this security issue.
Now let us see how it works. Iris scanning estimates the unique patterns in irises that are the colored rings or circles in people’s eyes. Bio-metric iris recognition scanners work by throwing light on the iris with an invisible infrared ray to pick up the unique patterns that are absolutely invisible to the naked eye. Iris scanners detect and disbar eyelids, eyelashes, and specular reflections that block parts of the iris. The final outcome is a set of pixels that only contains the iris. After that, the pattern of the eye’s lines and colors are examined to withdraw a bit pattern that conceals the information in the iris. That bit pattern is computerized and then differentiates with the stored templates in a database for the verification (one-to-one template matching) or identification (one-to-many template matching) process.
About the author: Mr. Shivam Garg, an alumni of 2nd year B.Tech - CS(AIML) at GLA University.