Our project is the real life implementation of iris recognition security system. As Iris recognition is regarded as the most reliable and accurate biometric identification system available. Practically a system designed with a very
Iris Recognition System
Our project is the real life implementation of iris recognition security system. As Iris recognition is regarded as the most reliable and accurate biometric identification system available. Practically a system designed with a very specific function in mind which works alongside a database in order to recognize the unique pattern in your iris using Neural Networks.
The goal of this project is to reduce human efforts and to ensure the security of the organization.
Our project will mainly focus on the following objectives:
Detection : Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input.
Segmentation : The circle Hough Transform (CHT) is a basic feature extraction technique used in digital image processing for detecting circles in imperfect images. The circle candidates are produced by “voting” in the Hough parameter space and then selecting local maxima in an accumulator matrix.
Feature Extraction : Daugman's method which finds the circular edge detector that based on smoothed integro-differential operators.
Local DB
Api's
Compared to other modalities, it has a significantly lower false acceptance rate and false rejection rate. It is a highly useful technology in areas such as border security, membership authentication, financial institutes, and information security to name just a few of its many uses.
Secondly , as it's cost effective so any body can buy it and everyone can uses the same right of securing themselves and their products.
We have made a System for the final evaluation test in which we will register user on run time and then locks and unlocks the safe by that registered user's Iris scanning on runtime.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Arduino Uno | Equipment | 2 | 2200 | 4400 |
| Arduino Camera | Equipment | 4 | 900 | 3600 |
| Batteries | Equipment | 4 | 1500 | 6000 |
| Arduino Mega | Equipment | 2 | 1800 | 3600 |
| Wiring and Led's | Equipment | 100 | 50 | 5000 |
| Raspberry Pi 3 | Equipment | 1 | 5500 | 5500 |
| Raspberry Pi | Equipment | 1 | 7030 | 7030 |
| Pi Camera | Equipment | 2 | 3800 | 7600 |
| Safe Box | Equipment | 1 | 2400 | 2400 |
| Web Cam | Equipment | 3 | 490 | 1470 |
| Lock | Equipment | 2 | 100 | 200 |
| TFT Shield | Equipment | 1 | 3000 | 3000 |
| AWS Cloud Services | Equipment | 1 | 20000 | 20000 |
| Fuel Consumption | Miscellaneous | 1 | 4000 | 4000 |
| Printing | Miscellaneous | 250 | 12 | 3000 |
| Courier Charges | Miscellaneous | 1 | 1000 | 1000 |
| Travelling & Shipments | Miscellaneous | 1 | 2000 | 2000 |
| Total in (Rs) | 79800 |
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