# Face tracking library

> AI-ranked search results for `face tracking` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 106 total matches; showing the top 30.

Explore on the web: https://awesome-repositories.com/q/face-tracking

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/face-tracking).**

## Results

- [cmu-perceptual-computing-lab/openpose](https://awesome-repositories.com/repository/cmu-perceptual-computing-lab-openpose.md) (34,145 ⭐) — OpenPose is a real-time pose estimation engine designed to detect and track human body, face, hand, and foot landmarks. It functions as a multi-person motion tracker, identifying the spatial coordinates of multiple individuals simultaneously within video streams or static images. Beyond two-dimensional detection, the software acts as a three-dimensional kinematics processor, reconstructing spatial movement data from single or multiple synchronized camera perspectives.

The system distinguishes itself through a bottom-up approach that utilizes part-affinity fields to associate body parts across
- [cleardusk/3ddfa](https://awesome-repositories.com/repository/cleardusk-3ddfa.md) (3,678 ⭐) — 3DDFA is a 3D face reconstruction tool that generates three-dimensional facial meshes and 68 structural landmarks from a single two-dimensional input image.

The project provides utilities for estimating facial pose and depth maps to determine the orientation and position of a face. It includes a geometry exporter to save reconstructed facial shapes into standard file formats for use in external 3D modeling software.

The software further covers automated face cropping using landmark-based and landmark-free identification methods, as well as the extraction of specialized mathematical represent
- [yadiraf/prnet](https://awesome-repositories.com/repository/yadiraf-prnet.md) (5,013 ⭐) — 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
- [mvig-sjtu/alphapose](https://awesome-repositories.com/repository/mvig-sjtu-alphapose.md) (8,583 ⭐) — AlphaPose is a deep learning pose estimation framework and PyTorch computer vision library designed for detecting and tracking human body, face, hand, and foot keypoints in images and videos. It provides a system for skeletal posture estimation and multi-person pose tracking.

The project implements tools for three-dimensional human pose reconstruction, generating joint positions and body mesh shapes from two-dimensional image data. It also includes a multi-person pose tracker capable of maintaining the identity of multiple people across consecutive video frames.

The framework covers a broad
- [tadasbaltrusaitis/openface](https://awesome-repositories.com/repository/tadasbaltrusaitis-openface.md) (7,690 ⭐) — 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
- [1adrianb/2d-and-3d-face-alignment](https://awesome-repositories.com/repository/1adrianb-2d-and-3d-face-alignment.md) (974 ⭐) — 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
- [hrnet/hrnet-facial-landmark-detection](https://awesome-repositories.com/repository/hrnet-hrnet-facial-landmark-detection.md) (1,135 ⭐) — 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
- [1adrianb/face-alignment](https://awesome-repositories.com/repository/1adrianb-face-alignment.md) (7,518 ⭐) — 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
- [kpzhang93/mtcnn_face_detection_alignment](https://awesome-repositories.com/repository/kpzhang93-mtcnn-face-detection-alignment.md) (2,863 ⭐) — 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
- [esimov/pigo](https://awesome-repositories.com/repository/esimov-pigo.md) (4,710 ⭐) — 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
- [leoxiaobin/deep-high-resolution-net.pytorch](https://awesome-repositories.com/repository/leoxiaobin-deep-high-resolution-net-pytorch.md) (4,479 ⭐) — 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.
- [itcoders/human-detection-and-tracking](https://awesome-repositories.com/repository/itcoders-human-detection-and-tracking.md) (874 ⭐) — 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
- [justadudewhohacks/face-recognition.js](https://awesome-repositories.com/repository/justadudewhohacks-face-recognition-js.md) (1,924 ⭐) — 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
- [auduno/clmtrackr](https://awesome-repositories.com/repository/auduno-clmtrackr.md) (6,504 ⭐) — 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
- [ageitgey/face_recognition](https://awesome-repositories.com/repository/ageitgey-face-recognition.md) (56,504 ⭐) — 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
- [open-mmlab/mmpose](https://awesome-repositories.com/repository/open-mmlab-mmpose.md) (7,374 ⭐) — MMPose is a PyTorch-based pose estimation toolbox and deep learning training pipeline designed for detecting 2D and 3D keypoints on humans, animals, and faces. It serves as a computer vision model zoo and a framework for both 2D pose estimation and 3D pose lifting.

The project is distinguished by its modular architecture and extensibility, employing a registry-based system and hierarchical configurations to allow for custom algorithm integration and model pipeline customization. It supports diverse estimation paradigms, including top-down, bottom-up, and two-stage pose lifting workflows.

The
- [exadel-inc/compreface](https://awesome-repositories.com/repository/exadel-inc-compreface.md) (7,782 ⭐) — 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
- [xlite-dev/lite.ai.toolkit](https://awesome-repositories.com/repository/xlite-dev-lite-ai-toolkit.md) (4,413 ⭐) — 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
- [cmusatyalab/openface](https://awesome-repositories.com/repository/cmusatyalab-openface.md) (15,398 ⭐) — 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
- [timesler/facenet-pytorch](https://awesome-repositories.com/repository/timesler-facenet-pytorch.md) (5,148 ⭐) — facenet-pytorch is a facial recognition library for PyTorch that provides pretrained neural networks for detecting faces and extracting facial embeddings. It includes an MTCNN face detector for locating faces and landmarks, alongside an InceptionResnet face encoder to convert facial images into high-dimensional vectors for identity verification.

The project provides tools for identity recognition by comparing facial embeddings using cosine similarity. It also supports facial video tracking to maintain identity consistency across consecutive frames and allows for the fine-tuning of pretrained
- [facebookresearch/detectron2](https://awesome-repositories.com/repository/facebookresearch-detectron2.md) (34,548 ⭐) — Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration.

The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati
- [facebookresearch/maskrcnn-benchmark](https://awesome-repositories.com/repository/facebookresearch-maskrcnn-benchmark.md) (9,370 ⭐) — This project is a modular PyTorch framework for training and evaluating object detection and instance segmentation models. It serves as a computer vision research tool and a deep learning inference engine designed to identify object locations, classes, and pixel-level masks within images.

The framework implements a two-stage inference pipeline that utilizes region proposal networks and a symmetric mask-head architecture. It provides specialized capabilities for instance segmentation, object bounding box detection, and human pose estimation via anatomical keypoint detection.

The system includ
- [justadudewhohacks/face-api.js](https://awesome-repositories.com/repository/justadudewhohacks-face-api-js.md) (17,869 ⭐) — 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
- [serengil/deepface](https://awesome-repositories.com/repository/serengil-deepface.md) (22,226 ⭐) — Deepface is a comprehensive deep learning library for facial recognition and demographic analysis. It provides a modular pipeline that handles the entire lifecycle of facial processing, including detection, geometric alignment, and the transformation of facial images into high-dimensional numerical vector embeddings for identity verification and similarity comparison.

The library distinguishes itself through a model ensemble approach, which combines predictions from multiple pre-trained neural networks to improve classification accuracy and reduce bias. It also integrates advanced security fe
- [kagami/go-face](https://awesome-repositories.com/repository/kagami-go-face.md) (1,189 ⭐) — 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
- [vipstone/faceai](https://awesome-repositories.com/repository/vipstone-faceai.md) (11,088 ⭐) — 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
- [accord-net/framework](https://awesome-repositories.com/repository/accord-net-framework.md) (4,540 ⭐) — This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision.

The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia
- [iperov/deepfacelive](https://awesome-repositories.com/repository/iperov-deepfacelive.md) (30,536 ⭐) — DeepFaceLive is a desktop application designed for real-time facial replacement and animation within live video streams. By utilizing deep learning models, the software performs high-speed identity mapping and facial feature analysis to transform video content as it is captured. The engine relies on GPU-accelerated inference to execute these complex image manipulation tasks at interactive frame rates.

The application distinguishes itself through a modular video processing pipeline that chains specialized tasks to maintain high throughput and low latency. It features a virtual camera streaming
- [jeeliz/jeelizfacefilter](https://awesome-repositories.com/repository/jeeliz-jeelizfacefilter.md) (2,883 ⭐) — 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
- [jeromeetienne/ar.js](https://awesome-repositories.com/repository/jeromeetienne-ar-js.md) (15,801 ⭐) — AR.js is a web-based augmented reality framework used to create immersive experiences directly in a browser using WebGL and WebRTC. It provides a cross-platform runtime that executes on mobile devices and head-mounted displays without requiring a dedicated application installation.

The framework includes a marker-based engine for detecting physical patterns or barcodes to anchor digital overlays, a location-based tool for positioning entities via GPS coordinates, and a face tracking library for anchoring content to human facial features. It supports multi-marker simultaneous tracking, custom