Projects sharing features with OpenCV Face Recognition

This is a Python facial recognition library designed to detect, encode, and identify human faces in images and video. It functions as a biometric identification tool that converts facial features into numerical encodings to compare and match identities. The library provides a computer vision command line interface for batch processing face detection and recognition tasks across image directories. It also supports a GPU accelerated vision API that utilizes CUDA and NVIDIA hardware to increase the speed of facial analysis and identification. Its capabilities cover human face detection and faci

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender

jeelizFaceFilter is a browser-based computer vision engine and WebGL face tracking library designed for AR filters and real-time facial movement tracking. It functions as a neural network face detector that identifies multiple faces and monitors mouth movements and rotation within a web browser. The system distinguishes itself through a model-swappable detection pipeline, allowing the exchange of neural network weights to balance accuracy and performance across different camera angles and devices. It features real-time lighting synchronization to match the illumination of 3D overlays with the

Pigo is a computer vision library written in Go for locating human faces in images and video streams. It provides tools for face detection, facial landmark identification, and pupil and eye localization. The project is implemented in pure Go to ensure portable execution without external dependencies. It supports compilation to WebAssembly, enabling face detection and image processing to run directly in web browsers without a backend. The library's capabilities include real-time face detection using classifier cascades and gaze tracking localization. It maps anatomical points on the face to a

Faceai is a computer vision toolkit designed for facial analysis, identity recognition, and image processing. It provides integrated engines for detecting human faces in static images and live video streams, matching facial encodings against identity databases, and mapping facial landmarks to understand geometric structure and alignment. The project enables real-time augmented reality applications, such as applying virtual makeup and digital accessories by scaling assets to detected facial coordinates. It also includes a suite for digital image restoration capable of removing noise, erasing w

This project is a computer vision framework designed for the detection, identification, and tracking of human subjects within video streams. It provides an integrated system for locating individuals, generating biometric models from image datasets, and maintaining identity labels across consecutive video frames. The system distinguishes itself through its ability to maintain identity persistence across multiple camera feeds. By utilizing deep learning inference to extract feature vector embeddings and applying motion prediction algorithms, it links unique identity signatures across disparate

This project is a computer vision system designed for real-time facial recognition and identity tracking using live camera feeds. It provides a framework for capturing, registering, and identifying multiple individuals simultaneously by comparing live video input against a local database of pre-registered facial descriptors. The system distinguishes itself through a performance-oriented processing pipeline that balances computational load during live analysis. By combining deep neural network feature extraction with centroid-based object tracking, the software maintains consistent identity la

This project is a Go library designed for facial detection, landmark mapping, and identity verification. It provides a toolkit for integrating computer vision capabilities into applications, enabling the automated identification and analysis of human faces within digital images. The library utilizes a deep residual network to transform facial data into compact vector representations, which are then compared using geometric distance calculations to confirm identities. It employs histogram-based object detection to locate facial structures and maps specific points on the face to define geometry

Human is a TensorFlow.js computer vision library used for face, body, and hand tracking within the browser or Node.js. It provides a framework for human pose and gesture tracking, facial recognition, and biometric liveness detection to verify a live human presence. The project distinguishes itself through a full suite of identity and motion tools, including a facial recognition framework that generates embeddings for similarity matching and a background segmenter for separating humans from their environment. It incorporates a liveness detector to prevent spoofing during facial analysis. The

CompreFace is a facial recognition system designed for human face detection, identification, and biometric identity verification. It provides a registry of known people and the ability to match faces in images against this database to determine a specific identity. The system extracts facial landmarks to map geometry and analyzes physical attributes including age, gender, and head pose. It can also verify whether two different images belong to the same individual. The project is implemented as a microservice-based deployment utilizing a REST API gateway and a PostgreSQL metadata store. It in

tensorrtx is a computer vision inference engine and model implementation library designed for graphics processor acceleration. It provides a framework for optimizing deep learning models through a GPU inference optimizer, a deep learning model converter for transforming weights from frameworks like TensorFlow and PyTorch, and a custom plugin library to implement operations not natively supported by the TensorRT API. The project distinguishes itself through a comprehensive collection of pre-defined network implementations, ranging from various YOLO versions and DETR transformers for object det

This repository is a comprehensive collection of reference implementations and sample libraries for the Universal Windows Platform. It provides practical examples of how to use Windows Runtime APIs to build cross-device applications, including detailed guidance on XAML-based declarative user interfaces and DirectX-integrated rendering. The project distinguishes itself by providing a wide array of hardware integration suites, covering low-level communication with USB, Serial, I2C, SPI, and GPIO peripherals. It includes specialized implementations for mixed reality holographic rendering, advanc

PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti

This project is a Java-based toolkit that integrates the OpenCV computer vision library into the Processing creative coding environment. It provides a programming interface designed to facilitate the inclusion of real-time image analysis and computer vision algorithms within interactive art installations and visual design projects. The library distinguishes itself by wrapping low-level C++ routines into a managed environment, allowing users to perform complex visual tasks through a simplified interface. It supports high-performance operations by sharing raw pixel data between the host environ

OpenCVSharp is a .NET library that wraps native OpenCV functions, providing C# developers with access to OpenCV's computer vision capabilities through an API that mirrors the native C/C++ style. It serves as a managed wrapper for image processing, feature detection, object detection, and image manipulation tasks, while also handling automatic disposal of unmanaged OpenCV resources like Mat objects to prevent memory leaks in .NET applications. The library enables keypoint detection and descriptor extraction using algorithms such as AKAZE, BRISK, or FAST, with brute-force or FLANN-based matchin

This is a collection of Python automation scripts and utility tools designed to handle repetitive technical tasks, system administration, and developer workflows. The project serves as a suite for task automation, data utility, and web automation. The collection includes specialized tools for multimedia processing, such as optical character recognition for extracting text from images, speech-to-text conversion, and real-time face and human body detection. It also features web scraping and monitoring capabilities to track product prices, fetch external API content, and automate interactions wi

This project is a scripting framework designed for task automation and computer vision processing. It functions as an automated messaging bot for communication platforms and a tool for detecting and identifying human faces within digital images. The framework utilizes a series of algorithms to isolate facial landmarks and perform pattern analysis on digital frames. It integrates with communication services to monitor incoming data streams, allowing for the execution of predefined logic in response to user requests. The system manages data through a sequential buffer to ensure orderly process

face.evoLVe is a deep learning library designed for the training and deployment of facial recognition models. It provides a comprehensive framework for converting facial images into numerical feature vectors, enabling identity verification and similarity analysis across large-scale datasets. The project facilitates the entire lifecycle of facial analysis, from dataset preparation and image standardization to distributed model training. It includes utilities for detecting facial landmarks and applying geometric transformations to ensure consistent input orientation, as well as data augmentatio

This project is a collection of reference implementations and demonstration projects covering computer vision, DevOps automation, distributed systems, and Java-based microservices. It provides a programming reference library and practical examples for building server-side applications, containerizing services, and orchestrating clusters. The repository features a comprehensive toolset for DevOps automation, including scripts and playbooks for CI/CD pipelines and automated cluster installation. It includes a computer vision demo project for image object detection and facial analysis, as well a

face-api.js is a TensorFlow.js face recognition library and browser-based computer vision API. It provides tools for performing face detection, recognition, and landmark prediction within browsers and Node.js. The library includes a biometric identity descriptor generator that creates numerical vectors to compare identity and similarity between images. It features a facial landmark detection tool for mapping sixty-eight specific coordinate points on a face, as well as an age and gender estimation model. Its capabilities cover real-time facial analysis, including the recognition of facial exp

This project is a JavaScript library designed for real-time face detection directly within a web browser. It functions as a machine learning model wrapper that enables developers to identify and track human faces in live video streams without the need for backend server processing. The library utilizes browser-native media access to stream raw camera data into application memory, where it performs pixel-level analysis. By leveraging a tensor-based inference engine and web-assembly acceleration, the tool executes complex neural network calculations locally to achieve high-performance computer

libfacedetection is a C++ face detection library and computer vision tool. It utilizes a neural network face detector to identify human faces in images and return bounding box coordinates. The library is designed for low latency and high throughput processing, enabling real-time face detection in image and video streams. It supports automated image analysis for identifying coordinates of human faces across large batches of photos and high-performance video processing.

node-opencv is a high-performance C++ native addon and bridge that connects Node.js applications to the OpenCV library. It serves as an image processing toolkit and computer vision library, allowing JavaScript code to execute vision algorithms and image manipulation operations through native bindings. The project provides specialized capabilities for face and shape detection, as well as face identity recognition using trained models. It includes tools for object motion tracking through optical flow and background subtraction, along with the ability to identify specific patterns and analyze sh

This library provides a deep learning framework for identifying human faces and extracting facial landmarks within digital images. It utilizes a multi-task convolutional neural network architecture to simultaneously perform face classification, bounding box regression, and landmark localization. The system processes images through three sequential stages of neural networks, incorporating image pyramid resizing to detect faces of varying scales. To ensure accuracy, it employs bounding box regression to refine coordinate predictions and non-maximum suppression to filter out redundant overlappin

Faceswap is a comprehensive framework for automated media manipulation and neural face synthesis. It provides a modular pipeline that manages the entire lifecycle of facial feature extraction, deep learning model training, and image conversion. By coordinating complex computer vision workflows, the system enables users to map facial identities between source and destination datasets while maintaining structural alignment and lighting consistency across video frames. The project distinguishes itself through a highly extensible plugin-based architecture that handles hardware-accelerated process

tracking.js is a browser computer vision library written in JavaScript for performing real-time image analysis and object tracking directly within a web browser. It functions as a real-time object tracker, a color tracking tool, and a face detection utility. The library enables the detection and monitoring of specific color ranges, human faces, and known visual patterns across consecutive video frames. It extracts visual features and descriptors from images to identify distinct landmarks for matching and tracking. The project covers broad computer vision capabilities, including the ability t

Azure Docs is the official technical documentation repository for Microsoft Azure, the cloud computing platform. It provides comprehensive guidance on the full spectrum of Azure services, covering everything from core infrastructure components like virtual machines, Kubernetes clusters, and serverless computing to platform services for AI, machine learning, data analytics, and storage. The documentation details how to provision, manage, and govern cloud resources at scale, including policy enforcement, identity management, and cost optimization. The documentation distinguishes Azure through i