A practice of verifying people’s identity by unique patterns of the iris is called iris recognition. These patterns are collected from the ring-shaped area over the eye pupil. In general, the iris region is in blue, green, and brown color along with complex patterns. Further, it helps you to perform a close investigation of the biological features of the iris region. Overall, it is best to identify the person identification using biometric authentication. This page is completely created for helping PhD / MS scholars or Final year students who wish to do iris recognition project using python!!!

Generally, an iris recognition project takes two or more iris images as input. In specific, these images are captured by high-resolution digital camera at IR / visible light. Next, it performs a sophisticated computer-assisted matching technique. This technique compares patterns of the input image with the already stored image in the database. By the by, the matching engine has the ability to compare millions of images without compromising quality. So, it is efficient than other traditional techniques. 

Implementing IRIS Recognition Projects using Python code

What are the four steps for an iris recognition system?

The fundamental steps involved in iris recognition system are four such as image collection, iris separation, feature extraction, and pattern matching. Among other biometric authentications, iris recognition is considered to be the most accurate person’s identification. Although it is treated as the best technique, it has few research challenges in implementing iris recognition in real-world scenarios. Some of the primary limitations of the iris recognition system are given as below, 

Limitations of Iris Recognition 

  • Localization of Eye Pupil Border / Inner Edge
  • Reduction of Specular Reflection
  • Minimization of Noise Eyelid, Eyelash, etc.
  • Localization of Iris Outer Edge

Now, we can see the important algorithms to identify complex iris patterns in any format of iris images. Each has a set of instructions to solve specific research problems. Here, we have also included the main purpose of each algorithm in iris recognition.

Majorly, it involves preprocessing, iris segmentation, feature extraction, and pattern matching to detect iris in biometric authentication. Specifically, each process is suggested with multiple algorithms and techniques. So, it is necessary to compare the purposes and efficiency of algorithms in advance of techniques confirmation. 

Iris Recognition Algorithms 

  • Low Contrast / Brightness
    • Adaptive Histogram Equalization
    • BPDFHE Technique 
  • Low Illumination 
    • CLAHE Technique
  • Noise (For instance: eyelash, pupil pixels, eyelids, light reflections, etc.)   
    • Multi-Thresholding Technique
    • Median Filter (MF)
  • Blurriness
    • Blur-based Kernel Computation
  • Reflections
    • Line Intensity Profile (LIP)
  • Specular Highlight by Light Reflection         
    • Threshold and Inpainting Technique 
    • Morphological-Retinex Technique
    • Gaussian-based Bilateral Filter Technique
    • Two-Dimensional Linear Interpolation
  • Low Precision over low-resolution (LR) Iris Detection        
    • Heterogeneous Metric Learning Technique
    • Stacked autoencoder (SAE) Technique 
    • Convolutional Neural Network (CNN) Technique
  • Obstructions by Eyelid and Eyelashes          
    • Hough Line Detector 
    • Gaussian Filter
  • Variation among Darker and Brighter Pixels 
    • Bottom-hat / Top-hat Filters

For illustration purposes, here we have taken “feature extraction” as an example. In this, we have highlighted all possible research solutions that are effective to extract essential iris features/patterns. Likewise, we also have a list of techniques for other key operations of iris recognition. One should choose the right algorithms for their proposed research issues based on their project objectives. If you are new to this field, then we are ready to guide you appropriately in your required phase of research. 

Feature Extraction Methods for Iris Recognition

  • Speeded Up Robust Features (SURF)
  • Linear Discriminant Analysis (LDA)
  • Scale-invariant Feature Transform (SIFT)
  • Principal Component Analysis (PCA)

Next, we can see significant python libraries used for iris recognition. Generally, Python is a scripting language that supports various libraries and functions for different image processing operations. These in-built libraries are efficient to implement both conventional and modern techniques. For instance, python is capable to support any sort of complex mathematical task using “math” library. Similarly, it collects several libraries which is easy to import into the developing project. Moreover, each library has a specific set of operations that are used for satisfying the requirements of the iris recognition system.

Imported Libraries in Python for Iris Recognition 

  • import os
  • import sys
  • import cv2
  • import gzip
  • import copy
  • import inspect
  • import math
  • import random
  • import itertools
  • import numpy 
  • import cPickle

Then, we can see a basic module that is widely used to implement iris recognition project using python. As well, it is said to be a G6_iris_recognition module which is highly preferred by developers. This package includes numerous math functions and libraries that support all sorts of image processing operations. By the by, it also helps you to develop a secure and fast iris detection solution.

 For your reference, here we have given you the installation command, packages needed to be installed, and supported operating system. Likewise, we also give software and python package installation procedure at the time of project delivery. 

Python Module for Iris Recognition 

Module – Gate6 Iris Recognition Package   

OS for installation 

  • Linux, Windows, and Mac

Install Command

  • $ pip install G6-iris-recognition

Supported Libraries 

  • scipy
  • matplotlib
  • numpy
  • requests
  • opencv-python
  • imutils
  • scikit-image
  • opencv-contrib-python

In addition, we have also shared with you the procedure to use python packages over the iris recognition system. In this, we have mentioned packages for both training and testing of iris images until acquiring the name of matched patterns person. 

Beyond this, there are loads of packages in the iris recognition project using python. Our developers are efficient to choose the appropriate package based on your project needs. Our ultimate goal is to minimize the number of code lines with accurate results. 

How to use Python Package for Iris Recognition? 

  • When all the required settings and configurations are made, import G6_iris_recognition module to begin your project execution.
  • import G6_iris_recognition
  • After completion of the import process, train input image collected from Input_database Folder
    • Input_database/
  • Then, train the encrypt module based on the below procedure
    • G6_iris_recognition.iris_model_train(train_DB_path,train_Encode_model_path)
    • train_DB_path – Input_database/
    • train_Encode_model_path – encodingModel/irisEncodings.pickle
  • When the training process is completed then move on to the test process using real-time images
    • iris_name=G6_iris_recognition.iris_model_test(test_Encode_model_path,real_world_image_path) 
    • test_Encode_model_path – encodingModel/irisEncodings.pickle
    • real_world_image_path – real-time_image_path
  • Return person’s name (if matches found, then return person’s name)
    • iris_name

Further, we have also listed out few key requirements of iris recognition. Most importantly, python is furnished with numerous libraries and modules for efficiently processing millions of images. These are the main reasons that developers are choosing python to implement iris recognition. 

To process images, collected images also need to satisfy the following requirements. Since it helps to process images with high accuracy and high speed. We ensure you that we strictly fulfill all these needs before processing images.

Python Requirements for Iris Recognition 

  • Capture images without direct sunlight
  • No sunglasses or lens are allowed in images
  • Require flawless images from the scanner
  • At least require 90% of eye iris image
  • For the training process, at least 5 clear images are needed 
  • Follow same resolution/size for all scanned images 
  • Alter parameters based on image noise, size, and quality
  • A set threshold value for matching hamming distance    

Iris recognition project using python takes 2 IR eye images as input and generates a matching score. If the matching score matches the threshold value then it is recognized as the same person. Further, we have also given you a step-by-step procedure to develop an iris recognition system using OpenCV. In our procedure, we also include the dataset, research problems with corresponding solutions, performance metrics, and system requirements. Further, all our recommended libraries and modules are efficient to implement the iris recognition system in simple manner.

Python Project for Iris Recognition using OpenCV

  • Train and Test of Images
    • Train CASIA iris image database, it comprises nearly 16000 eye images
    • These images are collected from 400 individuals
    • By this process, we perform 50000 test samples 
  • Localization of Iris
    • Initially localizes and detects iris region in both eye images
    • Then, it encrypts important features of every iris 
    • Next, implement SIFT algorithm using OpenCV in order to relate features
  • Extraction of Feature
    • Recognize and label local features over irises images using SIFT algorithm
  • Classification
    • When the matching score is equal to the threshold, the iris is detected as the same person
  • Performance Evaluation
    • When there is no match among images, the false accept rate is 0
    • When there is 95% match is found, a false reject rate is 5%

In addition, we can see datasets of the current iris recognition project using python. In the development phase of research, dataset selection is very vital similar to development tool and performance metrics selection. Since iris recognition is considered a data-intensive application which majority of processes are based on dataset. Therefore, take extra care while selecting dataset for your project. 

If you are connected with us, then our developers will take whole responsibility for your project range from tool selection to performance evaluation of your selected project. So, we have itemized some important iris recognition datasets. 

Datasets Available for Iris Recognition 

  • IITD
    • Dataset – 2200+ NIR iris images
    • Image Size – 340 x 260 pixels
    • Subjects – 220+ 
    • Database – IIT Delhi iris database
  • Specification
    • Collect short distance and front angle
    • Generate groundtruths using 1 operator
  • BioSec
    • Dataset – 3,200+ NIR images 
    • Image Size – 620 × 460 pixels 
    • Subjects – 200+
    • Segmentation Mask – 1,200 iris images 
  • UBIRIS.v2
    • Dataset – 11000+ images
    • Subjects – 50+
    • Segmentation Mask – 2200 iris images
    • Specification
      • Includes offaxis and frontal image 
      • Capture visible spectrum at various distance and obstructions
      • Generate groundtruths using 1 operator
  • CASIA-Iris-V4-Interval
    • Dataset – 2600+ iris images 
    • Image size – 340×260 pixels 
    • Subjects – 200+
    • Specification
      • Collect image in close distance using NIR light 
      • Generate groundtruths using 2 operators 
  • Nortedame-0405
    • Dataset – 64000+ NIR iris images
    • Image Size – 620 x 460 pixel
    • Specification
      • Solve real-time issues such as occlusion, off-axis, motion blur, etc.
      • Generate groundtruth using 1 operator
  • MobBIO
    • Dataset – 800+ images 
    • Image Size – 260 × 220 pixels 
    • Subject – 100+
    • Specification
      • Collect image over webcam using visible light 
      • Lightning conditions – Various Obstruction and Eye orientations 
      • Fixed distance over camera 
      • Generate groundtruths using database 

Last but not least, now we can see a few vital iris recognition projects using python. These topics are collected based on the current research interest of both scholar and final year students. Moreover, we also suggest topics that are sure to support future research directions. 

Also, we have framed different innovative topics in future research areas too. Beyond this list of topics, we have numerous creative iris recognition topics from every research possibility. Once you share your desired research area in iris recognition, we provide a huge number of unique project ideas from our collection. 

Top 10 Interesting IRIS Reocgnition Thesis Topics

Top 10 Research Topics in Iris Recognition Project using Python 

  • QC-LDPC codes for Iris Authentication 
  • Eye Animation and Iris Biometric Authentication
  • Precise Sclera Segmentation in U-Net 
  • Efficient Eye Segmentation in Shape Restricted Network 
  • CNN-based 3D Iris Recognition from Single Image 
  • Iris Segmentation using Circular Interpolation Technique 
  • DL-CNN and Gabor Filters for Iris Recognition
  • Eye Pupil Localization using Image Inpaint and Energy Map 
  • Deep Learning and Fuzzy Methods for Image Improvement in Iris Detection 
  • Multimodal Cancellable Biometric Templates for Weighted Graph Approach

On the whole, we help you to develop project ideas from both current and future research directions. Further, if you want to know other latest research trends of iris recognition then communicate with us. We are sure to provide the fullest support in every stage of your iris recognition project using python programming. We guarantee you that we make you create an unbelievable research contribution in your handpicked research area. So, contact us to create a positive impact in your research career through accurate results.