deepface vs FaceNet
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 TensorFlow implementation of the FaceNet face recognition model, with pretrained weights.
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 FaceNet if you need…
- Researchers reproducing the FaceNet paper with TensorFlow
- Developers who want pretrained face embedding models to build on
- Engineers training a face classifier on their own image set, following the linked wiki
- Students studying how face recognition models are trained and evaluated
Side-by-side
| deepface | FaceNet | |
|---|---|---|
| Type | Library | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 23.6k | 14.4k |
| Setup difficulty | Intermediate | Advanced |
| How you get it | Install with a package manager | Clone and build from source |
| Main language | Python | Python |
| Last code update | 6 days ago | 3 years 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.
FaceNet
This project is a TensorFlow version of FaceNet, a method that turns a photo of a face into a list of numbers (an embedding) so that photos of the same person end up close together. It includes training code, face alignment using MTCNN, and pretrained models reported to reach about 99% accuracy on the LFW benchmark. People use it as a starting point for building or testing face recognition systems.
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
FaceNet
- Researchers reproducing the FaceNet paper with TensorFlow
- Developers who want pretrained face embedding models to build on
- Engineers training a face classifier on their own image set, following the linked wiki
- Students studying how face recognition models are trained and evaluated
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
FaceNet
- Based on a well-known published paper (FaceNet), so the approach is documented and widely cited
- Ships pretrained models with reported LFW accuracy (0.9905 and 0.9965) and their training datasets
- Includes training scripts, face alignment code and test cases in the repository
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
FaceNet
- People who need a plug-and-play face unlock or photo tagging app, since the README describes research code, not an end-user product
- Teams needing current TensorFlow versions, because the README says it was tested only with TensorFlow r1.7, Python 2.7 and 3.5
- Anyone who needs guidance on legal or ethical use of face data, which the README does not cover beyond crediting dataset providers
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
FaceNet
- The README states compatibility only with TensorFlow r1.7, Ubuntu 14.04, and Python 2.7/3.5, which are very old, so setup on modern systems may be difficult
- Last code push was 2023-07-24 and there is no formal release, so maintenance status is unclear
- Some README sections are incomplete or point to external wiki pages for key details
Getting started
deepface
Install from PyPI (ONNX Runtime backend by default)
pip install deepfaceInstall with TensorFlow backend
pip install "deepface[tensorflow]"FaceNet
Clone the repository
git clone https://github.com/davidsandberg/facenet.gitFrequently asked questions
What is the difference between deepface and FaceNet?
deepface: Python library for face verification, face search, and estimating age, gender, emotion and race from photos. FaceNet: A TensorFlow implementation of the FaceNet face recognition model, with pretrained weights. deepface suits python developers prototyping face verification or face search, while FaceNet suits researchers reproducing the facenet paper with tensorflow.
Are deepface and FaceNet free?
deepface: Free and open source. FaceNet: Free and open source.
Which is easier to set up, deepface or FaceNet?
deepface is easier to get started with (rated intermediate); FaceNet is rated advanced and usually needs more setup.
Is deepface or FaceNet more popular?
deepface has more GitHub stars (23.6k) than FaceNet (14.4k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to deepface and FaceNet?
Other options people compare them with include face_recognition, face-api.js, ONNX Runtime, Recommenders, imgaug.
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.