Iris recognition is a type of human verification technique that extracts the patterns of the iris by using some pattern recognition algorithms. For both image and video-based biometric authentication, iris recognition can be implemented. This article provokes innovative research ideas and gives more information about Iris Recognition Project!!!

Iris includes complex patterns that are unique, stable, and visible from a distance. Retinal scanning is a distinct ocular-based biometric technique that employs the distinct patterns on a person’s retina vascular system and is sometimes mistaken with iris recognition. 

Implementing IRIS Recognition Project using Python Code

How Does Iris Recognition Works? 

  • Iris identification employs video camera technology with modest Near-Infrared Light (NIR) to capture photos of the iris. For this, the detail-rich, complicated structure is visible from the outside.
  • The identification of an individual or someone claiming to be that individual is possible thanks to digital templates encoded from these patterns by mathematical and statistical algorithms.
  • Matcher engines scan collections of enrolled templates at speeds measured in the millions of designs per second per (single-core) CPU, with very low false match rates.

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Flow for Iris Recognition

The right Eye and Left Eye are the two main characteristics of image acquisition. They are linked to the pre-processing step, which has two sections:

  • Iris Localization
  • Iris Normalisation

The primary one is CNN (Iris ConvNet), which has two characteristics. Match Score 1 and Match Score 2, both of which are closely related to Rank Fusion and Decision Making. 

What are the applications of iris recognition? 

  • It is a type of biometric technology similar to facial recognition and fingerprinting.
  • Proponents of iris scanning technology say that it enables law enforcement personnel to compare suspects’ iris pictures to an existing database of photographs in order to verify or confirm the subject’s identity.

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Important Properties of Iris Recognition

Extrinsic properties

  • Extrinsic characteristics have nothing to do with technology and are usually connected to the use case. For example, the effect of aging, pictures recorded in uncontrolled situations (for example, the specular reflections, effect of spectacles, outdoor imaging, and so on), and the potential of spoofing with an artificial iris.  

Intrinsic properties

  • It is built into the technology or the acquisition procedure. For example, the iris’s collection spectrum (near-infrared (NIR) or visible), the area of the iris in the picture, and the sensor type (dedicated or integrated into mobile device)

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Research Gaps in IRIS Recognition

  • Higher noise and sensitivity
  • Non- universality & reliability
  • Population coverage
  • Intra / inter-class variability
  • Vulnerability to spoofing    

 Where Iris Recognition works?

  • Blockchain assisted Access Control
  • Identity-based Authorization
  • Multi-factor Authentication
  • PUF-based Authentication
  • Privacy Preserved Computation   

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Algorithms for Iris Recognition


To avoid the artifacts and noise in the iris recognition, images are normalized. Pixel values i.e. color intensity and other types of techniques are applied to improve the normalization step. In order to facilitate the comparison of classification problems, the normalization procedure reduces artifacts. The main algorithms are,

  • Normal score
  • Standard score
  • Coefficient of variation
  • Min-Mix         


The picture must be precisely localized during the segmentation step so that the inner and outer edges of an iris may be represented as a circle. In the following, we highlighted some of the significant algorithms for iris recognition.

  • Hough transform
  • Watershed
  • Daughman’s method
  • Active Contour
  • Linear basis function
  • Adaptive level set 

Feature Detectors

After successfully normalizing and then segmenting the iris area, the following stage is to extract meaningful information from the normalized iris image. The retrieved characteristics are encoded in the iris template that is produced. The primary algorithms are as follows:

  • Sobel
  • Canny
  • Gabor filters
  • Determinant of Hessian
  • Wavelet transform 

Matching Techniques

At the test process, the templates created during the feature extraction stage are used to compare the similarity of two iris templates. This stage compares the similarity and dissimilarity of the two binary codes in order to make an acceptance or rejection judgment. The primary algorithms are as follows:

  • Approximate Nearest Neighbours
  • Overlap Similarity
  • Pearson Similarity
  • Cosine Similarity
  • Jaccard Similarity
  • Euclidean Distance

Machine learning approaches are used to concentrate on identification and feature extraction. Thus, the applications of machine learning and deep learning methodologies are increasing in medical image processing applications.

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Machine Learning Algorithms for Iris Recognition

  • Support Vector Machine
  • Decision Tree
  • Fuzzy Logic
  • K-Nearest neighbor
  • Naïve Bayes Classifier

Classification, Clustering, and Dimensionality Reduction processes are considered for iris recognition, which can be solved and performed using machine learning. In this area, we have used two models: Low-Density Separation Models and Graph Bases Algorithms. Transfer learning has been used to a variety of computer vision issues, including image segmentation, image classification, super-resolution, emotion analysis, image captioning, face recognition, and object identification, and has considerably outperformed previous techniques.

Similar to machine learning algorithms, deep learning plays a significant role in iris recognition. The major purpose of using deep learning algorithms is used to support for huge volume of datasets. Further, this provides higher performance in terms of accuracy, precision, and f-score. 

Deep Learning Algorithms for Iris Recognition Project

  • Radial Basis Function Networks (RBFNs)
  • Multilayer Perceptions (MLPs)
  • Deep Belief Network (DBNs)
  • Restricted Boltzmann Machines ( RBMs)
  • Autoencoders
  • Convolution Neural Networks ( CNNs)
  • Self-Organizing Maps (SOMs)
  • Long Short Term Memory Networks (LSTMs)
  • Recurrent Neural Networks ( RNNs)
  • Generative Adversarial Networks ( GANs)

There are numerous public datasets with a respectable amount of samples for iris recognition tasks, but most of them have a restricted number of examples per class, making training challenging. In the next section, we present a significant volume of datasets.

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Datasets for Iris Recognition


It contains 16,212 images (819 classes) captured by a dedicated iris scanner (OKI Irispass-H). The images are captured in an indoor environment with lamps (visible light illumination of the rooms) both on and off.


  • It has 2,000 classes that are collected by a specialized iris scanner (Irisking IKEMB -100).
  • The images are obtained in an interior environment, similar to the CASIA-Iris V4- lamp, with lamps (visible light lighting of the rooms) both on and off. With over 1,000 individuals, this was the first publicly available iris database. 

IIT Delhi Iris Database (IITD-V1)

It is confined to Indian topics and comprises 1,120 NIR pictures (224 classes) recorded in a restricted context. 

IIT Delhi Iris Database (IITD-V2)

We can use our system through its paces on the IIT Delhi iris database, which comprises 2240 iris pictures from 224 distinct people. These pictures have a resolution of 320×240 pixels. 

The CUHK Iris Image Dataset

  • The CUHK Based feature database comprises 254 NIR-captured pictures for 36 classes.
  • This database was one of the earliest freely available iris datasets, however, it is rather tiny. 

CASIA-Iris V4- Interval

It comprises 2,639 images of 395 classes taken in an indoor setting using a bespoke NIR camera. The database is well-suited for studying the fine details from the iris texture. 

CASIA Iris Database v.1

  • The National Laboratory of Pattern Recognition, Institute of Automation, CASIA database is created and collected from it.
  • The database was acquired using a custom-built NIR camera, and the authors manually processed the images by replacing the pupil area (and specular reflections) with a constant intensity value.
  • Because the manual involvement unduly simplified the situation, it is not suggested to utilize this database because it may be deceptive.
  • The database has been continually updated since the original release of CASIAv.1 to the present CASIA v.4.
  • Furthermore, the newer version’s structuring allows for the exploration of the influence of various effects, such as
    • Intra-class variations (CASIA-Iris-Lamp)
    • Correlations in twins (CASIA-Iris-Twins)
    • Influence of the capture distance (CASIA-Iris-Distance)
    • Cross-sensor compatibility
    • The influence of aging
    • Unconstrained capture

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