Iris Recognition using Machine Learning Technique

This project focuses on iris recognition using machine learning techniques. The application utilizes a Convolutional Neural Network (CNN) model to identify individuals based on their iris patterns.

Description

Iris recognition is a biometric identification method that uses pattern-recognition techniques based on high-resolution images of the irises of an individual's eyes. This project leverages machine learning to perform iris recognition. The main features include:

• Uploading a dataset of iris images.

• Loading a pre-trained CNN model.

• Predicting the identity of a person based on a provided test iris image.

How To Run

-Clone the repository & run the application main.py

python main.py

Features

• Upload Dataset

  Click on the "Upload CASIA Iris Image Dataset" button to load the dataset directory. The application will display the number of images and classes loaded.( For example , if you are using this model for office biometric upload your staff's iris as dataset) Below is the interface in which we have uploaded iris datasets.

  

• Load Model

  The application automatically loads the CNN model architecture and weights if present in the model directory.

• Predict Person

  Click on the "Upload Test Image & Predict Person" button to select a test image.If the Iris matches with the dataset no messages will be produced.Below is the collage image of our input image & output recived for an clear & existing iris image👇👇

But if any unclear iris or other images are uploaded then we get warning message as below "No eye iris is found". 👇👇

Troubleshooting

• Model Not Found : Ensure the model.json and model_weights.h5 files are present in the model directory.

• Training Data Not Found : Ensure the X.txt.npy and Y.txt.npy files are present in the model directory.

• No Iris Found : Ensure the test image is clear and contains a visible iris.

Acknowledgments

• CASIA Iris Image Database for providing the dataset used in this project.

• TensorFlow and Keras for providing the machine learning framework.

• Tkinter for the GUI components.