Projects sharing features with Facenet

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

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

by Feng Wang, Weiyang Liu, Haijun Liu, Jian Cheng

Implementation for in CVPR'17.

NormFace: L2 HyperSphere Embedding for Face Verification

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

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

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

Hide screen when boss is approaching.

PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics

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 branch is developed for deep face recognition

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

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

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

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

LibrePhotos is a self-hosted photo management system designed for organizing, tagging, and searching photos and videos stored on personal servers. It functions as an AI-powered media organizer and a multi-user media server, providing isolated accounts and secure permissions so multiple people can manage private collections on a single system. The project differentiates itself through a semantic image search engine that allows for natural language queries based on deep metadata analysis. It incorporates computer vision to automatically detect faces and identify objects, while utilizing geospat

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 computer vision-based security platform designed for real-time property surveillance and automated threat detection. It integrates live video stream management with neural network processing to monitor, track, and identify individuals within a monitored environment. The system distinguishes itself through local identity management, which allows users to train classifiers on specific individuals and store facial embeddings directly on the local system. By combining this biometric analysis with event-driven logic, the platform can differentiate between known persons and unknow

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 comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen

Frigate is a self-hosted network video recorder that functions as a private, local AI-powered vision engine. It manages video streams by performing real-time object detection, tracking, and classification directly on local hardware, ensuring that security monitoring and activity recording remain independent of cloud services. The system distinguishes itself through a modular, hardware-accelerated video pipeline that offloads intensive decoding and machine learning inference to dedicated GPUs, NPUs, or specialized accelerators like Coral TPUs and Hailo modules. It utilizes state-based object t

StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the

PhotoPrism is a self-hosted digital asset management platform designed to organize, classify, and manage large collections of photos and videos on personal infrastructure. It functions as a private alternative to cloud-based services, ensuring that all media remains under the user's control. The platform utilizes neural-network-based media analysis to automatically detect objects, faces, and locations, providing a comprehensive, AI-powered approach to library organization. The project distinguishes itself through its containerized architecture, which simplifies deployment and lifecycle manage

This project is a machine learning educational resource and implementation guide for Python. It provides a collection of executable code and notebooks that demonstrate predictive modeling, data analysis workflows, and the implementation of various machine learning algorithms. The repository features practical examples of classification, regression, and clustering tasks using Scikit-Learn, alongside tutorials for building and training deep learning architectures with TensorFlow. These include implementations of convolutional and recurrent networks. The content covers a broad range of capabili

This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l

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