Projects sharing features with Pigo

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

This project is a deep learning face classification system that detects human faces and classifies gender and emotion. It utilizes convolutional neural networks and computer vision tools to analyze facial attributes in both static images and live video streams. The system includes specialized classifiers for emotions based on the FER2013 dataset and gender based on IMDB datasets. These models are integrated into a containerized web service, allowing the classification logic to be exposed as an API that processes image data via network requests. The technical surface covers the entire pipelin

SeetaFaceEngine is a C++ face recognition engine designed to detect, align, and identify human faces. It functions as a native library that performs facial analysis without relying on external third-party software libraries. The system utilizes a convolutional neural network framework for facial feature extraction and identity matching, representing identities as numerical feature-vector embeddings. It employs a funnel-structured cascade schema for real-time face localization and stacked auto-encoder networks to normalize facial orientation through landmark alignment. The toolkit integrates

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

This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p

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

PRNet is a Python library for 3D facial reconstruction. It uses a deep learning regression model to predict 3D facial geometry and vertex colors from a single 2D input image to generate a textured mesh. The project provides tools for digital face swapping, allowing the replacement of a target face with a new image and blending textures to match the original pose. It also includes a framework for face texture swapping and blending to fit specific 3D poses. Additional capabilities cover facial analysis, including the detection and alignment of facial landmarks and the estimation of head pose a

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

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

This project is a deep learning framework designed for facial landmark detection. It functions as a computer vision library that provides the necessary routines to locate precise points on human faces within images by generating spatial probability maps. The architecture distinguishes itself through high-resolution parallel branching, which maintains detailed visual representations throughout the entire network. By utilizing multi-scale feature fusion, the model repeatedly exchanges information across these parallel streams to integrate fine-grained spatial details with broader semantic conte

This project is a computer vision library designed for facial landmark detection and alignment. It provides a framework for identifying and mapping specific points on a human face in both two-dimensional and three-dimensional space, enabling the normalization of facial geometry and orientation across diverse images. The system utilizes a deep learning approach to extract precise facial coordinates, supporting tasks such as expression analysis and geometric modeling. By employing a stacked hourglass architecture, the model performs multi-stage feature refinement to capture spatial relationship

OpenCV-Face-Recognition is a computer vision system designed to detect human faces and verify identities within live video streams and static images. It functions as a Python-based toolkit that processes visual data to locate facial boundaries and match detected features against a pre-trained database of profiles. The system utilizes a structured computer vision pipeline that integrates object detection with statistical analysis to perform identity verification. It employs pre-trained classifiers to scan for facial patterns and encodes facial textures into binary histograms to create represen

WebGazer is a JavaScript eye tracking library and browser-based computer vision tool that predicts a user's gaze coordinates on a screen in real time using a standard webcam. It functions as a client-side biometric tracker and accessibility input framework, mapping eye gaze to screen interactions to enable hands-free navigation and user interaction research. The system performs all video processing and gaze analysis locally within the web browser, removing the need for external servers. It employs regression-based mapping models to translate eye coordinates into screen pixels, utilizing a tra

OpenFace is an affective computing framework and facial behavior analysis toolkit designed for the extraction of facial features and the recognition of muscle movements to analyze human emotional behavior. It provides a research platform for analyzing human emotion and social interaction patterns through computer vision. The software implements a suite of tools for detecting facial landmarks, calculating head pose in 3D space relative to a camera, and tracking eye gaze by analyzing eye position and orientation. It also includes capabilities for facial action unit recognition to identify speci

This project is a computer vision tool designed to calculate the pitch, yaw, and roll of a human head in real time. It functions as a facial landmark detection library that identifies sixty-eight specific points on a face to enable precise orientation tracking from video streams. The system utilizes a high-performance inference engine to execute pre-trained machine learning models, ensuring that geometric analysis is performed efficiently on input frames. By mapping detected facial coordinates to a three-dimensional model, the software determines the camera-relative rotation of the head throu

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

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

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

Face-recognition.js is a computer vision software development kit for Node.js that provides tools for detecting, mapping, and identifying human faces within images and video streams. It functions as a bridge to high-performance native libraries, enabling developers to perform complex facial analysis tasks directly within JavaScript and TypeScript environments. The library distinguishes itself by combining deep learning inference with geometric landmark mapping. It utilizes pre-trained neural networks to extract facial feature vectors and employs Euclidean distance calculations to determine th

This project is a PyTorch implementation of a research architecture designed for high-resolution representation learning. It serves as a computer vision framework focused on precise keypoint detection, human pose estimation, and semantic image segmentation. The implementation provides specialized tools for identifying anatomical landmarks on the human body and predicting facial keypoint coordinates to analyze orientation and alignment. It utilizes a system of multi-resolution parallel streams and repeated multi-scale fusion to maintain high-resolution representations throughout the network.

Openface is a deep learning toolkit designed for facial recognition and identity verification. It provides a comprehensive pipeline for detecting faces, aligning landmarks, and transforming facial images into compact numerical vectors. By utilizing these embeddings, the system enables identity classification and similarity comparison through geometric distance calculations. The project distinguishes itself by integrating research-oriented diagnostic tools alongside its core recognition capabilities. It includes utilities for visualizing high-dimensional feature clusters, inspecting internal c

RustPython is a Python 3 compatible interpreter implemented in Rust. It functions as a scripting engine that can be embedded directly into host applications, allowing for the execution of dynamic scripts and the customization of software behavior within a memory-safe environment. The project distinguishes itself through its ability to bridge Python and JavaScript runtimes, enabling data exchange and function invocation across language boundaries. It also provides a portable execution environment by compiling Python code into WebAssembly, which allows for the execution of logic directly within

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

wasm-pack is a build tool and workflow orchestrator for compiling Rust code to WebAssembly. It coordinates the compilation process and generates the JavaScript glue code required to execute Rust logic within web browsers and server-side runtimes. The tool manages binary optimization to reduce file sizes and improve execution speed. It also functions as a package manager, bundling compiled WebAssembly modules into archives compatible with JavaScript registries and bundlers. The project covers language interoperability through the automatic generation of TypeScript type definitions. It further

Binaryen is a WebAssembly compiler toolchain and optimizer designed to transform, validate, and shrink binary modules. It provides a comprehensive intermediate representation framework that converts binary code into a single-assignment form to enable advanced program analysis and code transformation. The project includes a specialized transformation engine that applies iterative optimization passes to increase execution speed and reduce binary size. Additionally, it functions as a transpiler that translates WebAssembly binary modules into executable JavaScript for environments that lack nativ

Headtrackr is a JavaScript library and computer vision face tracker designed to monitor head position and orientation via a webcam. It provides a head tracking API that detects face location and spatial coordinates within a live video stream to create head-coupled perspective effects. The project functions as a tool for shifting 3D rendering perspectives in real time based on physical head movements. It utilizes WebRTC and media stream integration to capture video data and identify a user's face and rotation within a web browser. The library covers computer vision capabilities for face and h

Lancet is a comprehensive extension of the Go standard library, providing a collection of reusable functions and data structures designed to reduce boilerplate code in applications. It serves as a general-purpose toolkit across multiple domains, including concurrency, security, networking, and functional logic. The project distinguishes itself through specialized toolkits for Go concurrency, such as keyed locking and channel-based stream processing, and a dedicated functional programming kit that supports currying and function composition. It also includes a dedicated cryptography library imp