IRIS RECOGNITION BASED BANK LOCKER SECURITY SYSTEM USING RASPBERRY PI

Technologies that exploit biometrics have the potential for application to the identi?cation and veri?cation of individuals for controlling access to secured areas or materials. A wide variety of biometrics have been marshaled in support of this challenge. Resulting systems include those based on au

Project Title

IRIS RECOGNITION BASED BANK LOCKER SECURITY SYSTEM USING RASPBERRY PI

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

Technologies that exploit biometrics have the potential for application to the identi?cation and veri?cation of individuals for controlling access to secured areas or materials. A wide variety of biometrics have been marshaled in support of this challenge. Resulting systems include those based on automated recognition of retinal vasculature, ?ngerprints, hand shape, handwritten signature, and voice. Iris is one of the most useful biometric traits for person identi?cation due to its high stability and uncertainty.Technologies that exploit biometrics have the potential for application to the identi?cation and veri?cation of individuals for controlling access to secured areas or materials. A wide variety of biometrics have been marshaled in support of this challenge. Resulting systems include those based on automated recognition of retinal vasculature, ?ngerprints, hand shape, handwritten signature, and voice. Iris is one of the most useful biometric traits for person identi?cation due to its high stability and uncertainty.

Project Objectives

The general objective of this work is to create an iris recognition technology using Daugman algorithm implemented in Raspberry Pi.

The specific objectives are as follows:

Integrating OpenCV with Raspberry Pi

Python scripting for Computer Vision (Image processing)

To create a device that can perform the iris recognition technology.

Complete Hardware integrated with OpenCV via camera live capturing

Installing bank locker security systems with Raspberry Pi

Project Implementation Method

Image Processing and deep learning are used for the implementation of the project.

Programing language Python is used to impliment Image Processing and deep learning by using open CV on the Ruspbarry pi to complete the project.

Benefits of the Project

Biometrics can be called as the parts of the human body which give a unique identity to an individual. Biometric, or biometric identifiers are the distinctive, measurable characteristics used to label and describe individuals. Examples of biometric identifiers are Fingerprints, Hand-Writing, Voice, Face, Retina, Palm lines etc. All these systems are not reliable in this era so we proposed a new cost effective Iris Detection System which is most reliable system of all the mentioned systems. 

Technical Details of Final Deliverable

Technicaly the project start working by taking the Image  of the Iris is taken by the camera of the raspberry pi and then this image is converted into greyscale image.

then  locate the center of the pupil. Once located, it will determine the diameter of the pupil. Next, it will locate and determine the outer edge diameter of the iris, and then finding the useful region of the iris, applying a series of Gabor filters to the pixels found in the iris, with the use of polar coordinates, and creation of code for the iris. And finally, Hamming Distance is used to determine if an iris is within the database or recognized. Improvements are made to make the algorithm more accurate and faster in terms of performance.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Security

Other Industries

Finance

Core Technology

Others

Other Technologies

Wearables and Implantables

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

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