face_recognition vs faceai
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
A Python library and command-line tool for finding and recognizing faces in photos.
Open source · MIT
Beginner-level Python demos for face, video, and text detection built on OpenCV and Dlib.
The short answer
Choose face_recognition if you need…
- Python developers prototyping photo sorting or face matching
- Hobbyists who want to label people across a folder of personal photos
- Researchers who need a simple baseline face recognition pipeline
- Developers adding face detection to a script or small app
Choose faceai if you need…
- Students learning basic computer vision with Python
- Developers who want working examples of OpenCV and Dlib face detection
- Readers who can follow Chinese-language tutorials
Side-by-side
| face_recognition | faceai | |
|---|---|---|
| Type | Library | Learning resource |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 56.9k | 11.1k |
| Setup difficulty | Intermediate | Beginner |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | Python |
| Last code update | 4 months ago | 7 years ago |
What each one is
face_recognition
face_recognition is a Python library that detects faces in images, locates facial features like eyes and nose, and compares faces to tell whether two images show the same person. It also ships a command-line tool that can match a folder of unknown photos against a folder of labeled people. The README says it is built on dlib's deep-learning face recognition model, which it reports at 99.38% accuracy on the Labeled Faces in the Wild benchmark.
faceai
faceai is a Python project of tutorials and example code for detecting and recognizing faces in images and video. The README also covers features like drawing face outlines, adding hats and makeup to faces, gender and emotion recognition, OCR text recognition, and image inpainting. It is written in Chinese and is described by its author as an introductory project.
Best for
face_recognition
- Python developers prototyping photo sorting or face matching
- Hobbyists who want to label people across a folder of personal photos
- Researchers who need a simple baseline face recognition pipeline
- Developers adding face detection to a script or small app
faceai
- Students learning basic computer vision with Python
- Developers who want working examples of OpenCV and Dlib face detection
- Readers who can follow Chinese-language tutorials
Strengths
face_recognition
- Very short Python API for detection, landmarks and comparison
- Command-line tools work on whole folders of images
- MIT licensed and widely used, with a large star count
faceai
- Covers many basic computer vision tasks in one project
- Tutorials are organized by feature and linked from the README
- Uses widely known libraries such as OpenCV, Dlib, and Keras
Not ideal for
face_recognition
- Anyone needing official Windows support, since the README says Windows is not officially supported
- Projects that need to run without installing dlib and cmake, since installation is the hardest part according to the README
- Surveillance or identification of people without their consent, which raises serious privacy and legal concerns the README does not address
faceai
- Production systems, since the README documents a 2018-era setup and the repository's last push was in April 2020
- Readers who cannot read Chinese, since the main docs are in Chinese (an English README is linked but not included here)
- People who need the setup to run on current Python or library versions, since the README lists Python 3.6.4 and TensorFlow 1.8.0
Trade-offs
face_recognition
- Installation requires dlib and cmake, which the README describes as a multi-step process
- Windows is not officially supported and the README notes a silent failure bug on Jetson Nano unless a dlib line is edited
- README examples and install instructions reference older Python versions (3.3+ or 2.7), so modern setup may need checking
faceai
- Dependencies are pinned to old versions (Python 3.6.4, TensorFlow 1.8.0), which may be hard to install today
- Several features are marked unfinished, including eye tracking and face swapping
- Last push was April 2020, so it may not reflect current library APIs
Getting started
face_recognition
Install cmake (macOS, per README)
brew install cmakeInstall the library from PyPI
pip3 install face_recognitionfaceai
Clone the repository
git clone https://github.com/vipstone/faceaiFrequently asked questions
What is the difference between face_recognition and faceai?
face_recognition: A Python library and command-line tool for finding and recognizing faces in photos. faceai: Beginner-level Python demos for face, video, and text detection built on OpenCV and Dlib. face_recognition suits python developers prototyping photo sorting or face matching, while faceai suits students learning basic computer vision with python.
Are face_recognition and faceai free?
face_recognition: Free and open source. faceai: Free and open source.
Which is easier to set up, face_recognition or faceai?
faceai is easier to get started with (rated beginner); face_recognition is rated intermediate and usually needs more setup.
Is face_recognition or faceai more popular?
face_recognition has more GitHub stars (56.9k) than faceai (11.1k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to face_recognition and faceai?
Other options people compare them with include deepface, face-api.js.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.