Package: open-iris
IRIS: Iris Recognition Inference SystemPackage documentation•Getting startedtutorial•Hugging Face repo•IRIS blog postTable of contentsAboutQuickstartInstallationSetup for developmentRunning inferenceProject structureExample notebooksDocumentationIssues, pull requests and feature requestsLicenseResourcesAboutWelcome to Worldcoin's Iris Recognition Inference System (IRIS) project, an advanced iris recognition pipeline designed for robust and secure biometric verification. This project leverages state-of-the-art computer vision and machine learning techniques to provide accurate and efficient iris recognition system.Iris recognition is a powerful biometric technology that identifies individuals based on the unique patterns within the iris of the eye. IRIS package aims to make iris recognition accessible and enable further advancement in the field.Project features highlights are:Large-Scale Verification: Capable of verifying uniqueness among billions of users.High-Performance Iris Segmentation: Accurate segmentation of iris regions for precise feature extraction.Scalable Matching Algorithm: Robust matching algorithm designed for scalability without compromising accuracy.User-Friendly Integration: Simple integration into applications that demand seamless biometric verification.High-level iris recognition pipeline steps overview:Iris Image Input: Provide an iris image for verification.Iris Segmentation: Identify and isolate the iris region within the image.Feature Extraction: Extract unique features from the iris to create a template.Scalable Matching: Efficiently compare extracted features for large-scale uniqueness verification.Result: Receive the verification result with a confidence score, enabling secure and scalable authentication.The Worldcoin system utilizes iris recognition algorithm for verifying uniqueness in a challenging environment, involving billions of individuals. This entails a detailed exploration of the Worldcoin biometric pipeline, a system that confirms uniqueness through the encoding of iris texture into an iris code.More detailed pipeline overview can be found in ourblog postdedicated to IRIS project.DisclaimerThe Iris Recognition Inference System (IRIS) software repository is owned and maintained by the Worldcoin Foundation, the steward of the Worldcoin protocol; the repository is not affiliated with any other project or service providerQuickstartInstallationInstallation is as simple as runningpip installwith specifyingIRIS_ENVinstallation global flag (IRIS_ENVflag may be skipped ifirisis installed from PyPl server but this option is only available whenirisis installed on local machine). TheIRIS_ENVflag is used to indicate an "environment" in which package is meant to work. Possible options are:SERVER- For installingirispackage with dependecies required for running an inference on a local machines.# On a local machinepipinstallopen-iris# or directly from GitHubIRIS_ENV=SERVERpipinstallgit+https://github.com/worldcoin/open-iris.gitORB- For installingirispackage with dependecies required for running an inference on the Orb.# On the OrbIRIS_ENV=ORBpipinstallgit+https://github.com/worldcoin/open-iris.gitDEV- For installing iris package together with packages necessary for development ofirispackage.# For developmentIRIS_ENV=DEVpipinstallgit+https://github.com/worldcoin/open-iris.gitAfter successfully installingiris, verify your installation by attempting to import.python3-c"import iris; print(iris.__version__)"Setup for developmentAcondaenvironment simplifies the setup process for developing on theirispackage. Thiscondaenvironment ensures a seamless and consistent setup for contributors, reducing the complexity of dependency management. By utilizingconda, developers can easily replicate the development environment across different systems, minimizing potential setup obstacles. This approach aims to make it straightforward for anyone interested in contributing to quickly set up and engage in the development ofirispackage.# Clone the iris repogitclonehttps://github.com/worldcoin/open-iris# Go to the repo directorycdopen-iris# Create and activate conda environmentIRIS_ENV=DEVcondaenvcreate-f./conda/environment_dev.yml
condaactivateiris_dev# (Optional, but recommended) Install git hooks to preserve code format consistencypre-commitinstall
nb-cleanadd-filter--remove-empty-cellsRunning inferenceA simple inference run can be achived by running source code below.importcv2importiris# 1. Create IRISPipeline objectiris_pipeline=iris.IRISPipeline()# 2. Load IR image of an eyeimg_pixels=cv2.imread("/path/to/ir/image",cv2.IMREAD_GRAYSCALE)# 3. Perform inference# Options for the `eye_side` argument are: ["left", "right"]output=iris_pipeline(img_data=img_pixels,eye_side="left")To fully explore and understand the extensive capabilities of the iris package, visit theExample notebookssection. Here, you'll find a collection of Jupyter Notebooks that serve as valuable resources, offering practical guides and real-world examples to provide a comprehensive insight into the rich functionalities and potential applications of theirispackage.Project structureTheirispackage features a structured design with modular components, enhancing code organization and scalability.ModuleDescriptionirisan iris recognition packageiris.callbacksa module that implements callbacks used to customize on execute start and end behaviours of node or pipeline calliris.ioa module that contains dataclasses and errors that flow through iris recognition pipeline when callediris.nodesa module that contains implementation of iris recognition pipeline nodesiris.orchestrationa module that contains iris recognition pipeline's orchestration support mechanismsiris.pipelinesa module that contains implementation of iris recognition pipelinesiris.utilsa module that contains utilities used throughout the code base and modules usefull for outputs analysisExample notebooksThe Jupyter Notebooks provided present practical guides and real-world instances to demonstrate the complete capabilities of theirispackage.Getting startedConfiguring custom pipelineDocumentationFor detailed documentation, including installation instructions, usage guidelines, and configuration options, please refer to the IRIS projectdocumentation.Issues, pull requests and feature requestsIf you have any question or you found a bug or you feel like some feature is missing, please don't hesitate to file a new issue, discussion or PR with respective title and description.
Any suggestion for potential project improvements are and will always be welcome!Feel free to reach out to any projectcontributor!LicenseThis project is licensed under theMIT license.Resources"Iris Recognition Inference System""Iris feature extraction with 2D Gabor wavelets""How iris recognition works"