DOCSEARCHbyAlgolia
Submit Docs

InsightFace

repository·master·Indexed 12 days ago

https://github.com/deepinsight/insightface

A comprehensive 2D and 3D face analysis toolbox providing state-of-the-art algorithms for detection, recognition, and alignment. It includes tools for facial landmark prediction via coordinate regression (e.g., 2d106det), heatmap-based alignment using Stacked Dense U-Nets (SDU), and face attribute analysis for gender and age estimation.

Tokens
119.8K
Snippets
306
Records
550
Agent score
95%

What's inside InsightFace

  1. Overview of InsightFace

    master
    InsightFace is an open-source 2D and 3D deep face analysis toolbox. It provides state-of-the-art algorithms for face recognition, face detection, and face alignment, optimized for both training and deployment. The project is primarily based on PyTorch and MXNet.

  2. Overview of MFR-Ongoing (IFRT)

    master

    MFR-Ongoing (also known as IFRT - InsightFace Recognition Test) is an ongoing version of the ICCV-2021 Masked Face Recognition Challenge. It evaluates face recognition algorithms using non-celebrity testsets to ensure fair performance assessment, avoiding the overlap common with datasets like MS1M or CASIA.

    Key evaluation areas include:

    • Accuracy between masked and non-masked faces.
    • Accuracy among children (ages 2-16).
    • Accuracy of globalized multi-racial benchmarks.
    • Performance on public benchmarks like IJBC, LFW, CFPFP, and AgeDB.

  3. Overview of BlazeFace-Paddle face detection

    master

    Arcface-Paddle is an open-source deep face detection and recognition tool implemented using PaddlePaddle. This specific module focuses on face detection and is developed based on PaddleDetection.

    Key models available in the library include:

    • BlazeFace: Used for face detection.
    • ArcFace: Used for face recognition.
    • MobileFace: Used for face recognition.

    For face recognition details, refer to the recognition/arcface_paddle directory. For deployment using PaddleInference Whl packages, see the external insight-face-paddle repository.

  4. Overview of the ICCV21-MFR Challenge

    master

    The Masked Face Recognition Challenge & Workshop (MFR) is part of the ICCV 2021 conference. It features an InsightFace track and a Webface260M track. The challenge focuses on evaluating face recognition accuracy across three specific scenarios: masked vs. non-masked faces, children (ages 2-16), and globalized multi-racial benchmarks.

    Key Resources:

  5. Overview of Face Alignment methods in InsightFace

    master

    InsightFace provides several face alignment methods designed for 2D and 3D face analysis. These methods are used to identify and localize facial landmarks. The supported methods include:

    • SDUNets (BMVC'2018): A heatmap-based approach.
    • SimpleRegression: A coordinate-based regression approach.
    • Alignment By Face Synthetics: An approach utilizing synthetic face data.

    For detailed information on the specific datasets used for training and the evaluation protocols used to measure performance, refer to the dedicated datasets and evaluation documentation pages within the repository.

  6. Overview of Face Recognition methods in InsightFace

    master
    InsightFace provides a collection of face recognition methods implemented across various deep learning frameworks. These methods are designed for high-performance 2D and 3D face analysis. The available implementations include versions for PyTorch, MXNet, PaddlePaddle, and OneFlow, covering several state-of-the-art research papers from CVPR, ECCV, and NeurIPS.

  8. Overview of Arcface-Paddle

    master

    Arcface-Paddle is an open-source deep face detection and recognition toolkit powered by PaddlePaddle. It provides pretrained models for:

    • Face Detection: BlazeFace.
    • Face Recognition: ArcFace and MobileFace.

    This specific toolkit is primarily focused on face recognition tasks. For face detection-specific tutorials, refer to the BlazeFace Paddle documentation. For inference using Whl packages with PaddleInference, refer to the external insight-face-paddle repository.

  9. Overview of Arcface-Paddle Face Detection

    master

    Arcface-Paddle

    Arcface-Paddle is an open-source deep face detection and recognition toolkit powered by PaddlePaddle.

    For face detection tasks, this toolkit utilizes PaddleDetection and provides the BlazeFace model.

    Key Models Available:

    • BlazeFace: For face detection.
    • ArcFace: For face recognition.
    • MobileFace: For face recognition.

    Note: This specific tutorial focuses on face detection. For face recognition tasks, refer to the ArcFace-Paddle recognition tutorial. For Whl package inference using PaddleInference, see the external insight-face-paddle repository.

  10. Overview of InspireFace features

    master

    InspireFace is a cross-platform face recognition SDK written in C/C++. It provides a comprehensive suite of facial analysis capabilities including:

    • Face Detection & Tracking: High-precision detection and real-time tracking.
    • Face Recognition: Feature extraction and comparison.
    • Landmark Detection: Localization of facial landmarks.
    • Face Attributes: Recognition of age, gender, and emotion.
    • Liveness Detection: Support for both silent and cooperative liveness detection.
    • Mask Detection: Ability to detect if a face is wearing a mask.
    • Quality Assessment: Evaluation of face image quality.
    • Pose Estimation: Estimation of face pose angles.

  11. Available Face Detection Datasets

    master

    InsightFace utilizes several standard datasets for training and testing face detection models. These datasets are categorized into Training Datasets and Test Datasets.

    Training Datasets

    • WiderFace: A large-scale dataset used for training robust face detection models.

    Test Datasets

    • WiderFace: Used for evaluating detection performance across various scales and environments.
    • FDDB (Face Detection Benchmark): A standard benchmark for face detection.
    • AFW (Annotated Faces in the Wild): Used for testing detection and alignment.
    • PASCAL FACE: A dataset derived from the PASCAL VOC challenge.
    • MALF (Multi-Attribute Learning Face): Used for evaluation in specific face-related tasks.

  12. InsightFace Server Core Capabilities

    master

    InsightFace Server is a privacy-oriented, self-hosted alternative to AWS Rekognition. It provides a complete pipeline for face analysis within a single container using SQLite for persistence.

    Key Features:

    • Face Analysis: SCRFD detection, 5 landmarks, alignment, ArcFace embeddings, L2 normalization, and cosine similarity.
    • Data Model: Uses a Collection -> Person -> FaceSample hierarchy. Collections are bound to specific models.
    • Search Performance: Supports high-speed GPU search with INT8 quantization (up to 58.9M 512-dim vectors on an RTX 5090) with minimal accuracy loss.
    • Registration Modes (review_mode): off, standard, or strict.
    • API & UI: 29 snake_case REST operations under /v1 and a multi-lingual Web UI (Dashboard, Collections, People, Detect, Compare, Search, RTSP monitoring, etc.).
    • RTSP Monitoring: Server-side monitors with memory-limited events and optional MJPEG previews.