deepface vs face_recognition
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
Python library for face verification, face search, and estimating age, gender, emotion and race from photos.
Open source · MIT
A Python library and command-line tool for finding and recognizing faces in photos.
The short answer
Choose deepface if you need…
- Python developers prototyping face verification or face search
- Researchers comparing several face recognition models through one interface
- Teams that want to store face embeddings in an existing database such as Postgres or Redis
- Hobbyists who want to run face analysis on their own photos locally
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
Side-by-side
| deepface | face_recognition | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 23.6k | 56.9k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | Python |
| Last code update | 5 days ago | 4 months ago |
What each one is
deepface
DeepFace is a Python library that compares faces in images to tell whether two photos show the same person, searches a folder or database for a matching face, and estimates attributes such as age, gender, emotion and race. It wraps several existing face recognition models behind a few simple function calls, so you do not need to build the detection and comparison pipeline yourself. It is used by developers who want face features without writing the underlying deep learning code.
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.
Best for
deepface
- Python developers prototyping face verification or face search
- Researchers comparing several face recognition models through one interface
- Teams that want to store face embeddings in an existing database such as Postgres or Redis
- Hobbyists who want to run face analysis on their own photos locally
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
Strengths
deepface
- Wraps multiple recognition models (for example VGG-Face, FaceNet, ArcFace) behind a consistent interface
- Reduces the pipeline to single function calls, per the README
- Supports many database backends for scalable lookup, and is MIT licensed
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
Not ideal for
deepface
- Anyone needing a legally vetted biometric system: the README does not address compliance, consent or accuracy guarantees for production identification
- People who do not write Python, since the README only documents Python usage
- Users with limited disk space who want the TensorFlow or PyTorch backends, which the README says add one to several GB
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
Trade-offs
deepface
- The README's accuracy comparison to humans is a self-reported benchmark that it does not independently verify here; results depend on the model and images used
- Default ONNX install is lighter than TensorFlow or PyTorch, but the backend choice still affects setup size and behavior
- Face analysis of emotion, age, race and gender is inherently uncertain and the README gives no caveats about bias or error rates
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
Getting started
deepface
Install from PyPI (ONNX Runtime backend by default)
pip install deepfaceInstall with TensorFlow backend
pip install "deepface[tensorflow]"face_recognition
Install cmake (macOS, per README)
brew install cmakeInstall the library from PyPI
pip3 install face_recognitionFrequently asked questions
What is the difference between deepface and face_recognition?
deepface: Python library for face verification, face search, and estimating age, gender, emotion and race from photos. face_recognition: A Python library and command-line tool for finding and recognizing faces in photos. deepface suits python developers prototyping face verification or face search, while face_recognition suits python developers prototyping photo sorting or face matching.
Are deepface and face_recognition free?
deepface: Free and open source. face_recognition: Free and open source.
Is deepface or face_recognition more popular?
face_recognition has more GitHub stars (56.9k) than deepface (23.6k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to deepface and face_recognition?
Other options people compare them with include face-api.js, ONNX Runtime, Recommenders, imgaug, Weights & Biases.
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.