State of the art deep face analysis library

InsightFace is an open source 2D&3D deep face analysis library.

Why InsightFace

InsightFace is an integrated Python library for 2D&3D face analysis. InsightFace efficiently implements a rich variety of state of the art algorithms of face recognition, face detection and face alignment, which optimized for both training and deployment. Research institute and industrial organization can get benefits from InsightFace library.


2021.04: Rank 4 of NIST-FRVT 1:1, see leaderboard.

2021.01: Rank 4 of 2020 top 10 trending AI libraries, from paperswithcode.

2019.08: Achieved 2nd place at WIDER Face Detection Challenge 2019.

2019.04: RetinaFace obtains state-of-the-art results on WIDERFace dataset.

2019.01: Achieved 1st place at iQIYI VID Challenge.

It is free and open source

The code of InsightFace is MIT licensed. See the project on GitHub.

Contact Us:

Face Recognition Projects


ArcFace is the state of the art face recognition approach which accepted on CVPR 2019.

See the ArcFace project page.


SubCenter-ArcFace is a face recognition approach on large-scale noisy web faces which accepted on ECCV 2020.

See the SubCenter-ArcFace project page.


VPL(Variational Prototype Learning for Deep Face Recognition) is a face recognition approach which accepted on CVPR 2021.

See the VPL project page.


Partial-FC is a large-scale training framework for face recognition.

See the Partial-FC project page.

Face Detection Projects


RetinaFace is the state of the art multi-tasks face detection approach which accepted on CVPR 2020.

See the RetinaFace project page.


SCRFD is an efficient high accuracy face detection approach.

See the SCRFD project page.

Face Alignment Projects


Stacked dense u-nets is a face alignment approach which accepted on BMVC 2018.

See the SDUNet project page.


CoordinateReg is an experimental face alignment approach for fast and accurate inference.

See the CoordinateReg project page.

Challenges of InsightFace


IFRT is a globalised fair benchmark for face recognition algorithms. IFRT evaluates the algorithm performance on worldwide web pictures which contain various sex, age and race groups.

See the IFRT challenge page.

Lightweight Face Recognition Challenge

The Lightweight Face Recognition Challenge & Workshop will be held in conjunction with the International Conference on Computer Vision (ICCV) 2019, Seoul Korea.

See the LFR challenge page.

Getting started

Code of InsightFace requires Python 3.6 or higher. To install the library from PyPI run
pip install -U insightface