deepface vs face-api.js
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
JavaScript library that detects faces and recognizes facial features in browsers and Node.js.
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-api.js if you need…
- Web developers adding face detection to a browser page
- Developers building webcam demos that track faces or expressions
- Node.js developers who want server-side face detection with tensorflow.js
- Learners exploring face recognition in JavaScript
Side-by-side
| deepface | face-api.js | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 23.6k | 18k |
| Setup difficulty | Intermediate | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | TypeScript |
| 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.
face-api.js
face-api.js is a JavaScript library for finding faces in images and video and reading facial features from them, such as landmarks, expressions, and estimated age and gender. It runs on top of tensorflow.js, so it can work in a web browser or in Node.js. Developers use it to add face-related features to web pages or server-side scripts without writing their own machine learning code.
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-api.js
- Web developers adding face detection to a browser page
- Developers building webcam demos that track faces or expressions
- Node.js developers who want server-side face detection with tensorflow.js
- Learners exploring face recognition in JavaScript
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-api.js
- Runs directly in the browser, so images can stay on the user's device
- Covers several tasks in one API: detection, landmarks, recognition, expressions, age and gender
- MIT licensed with a large community and many tutorials listed in the README
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-api.js
- Projects needing the latest research models, since the README does not describe model updates and the last push shown is from January 2024
- Teams that need a fully supported commercial product with guaranteed accuracy, since the README makes no such claims
- Anyone who needs face identification at large scale, where a dedicated server-side system would be more appropriate
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-api.js
- Model files must be downloaded and served alongside the app, which adds load size
- The README notes that installing the optional native tfjs-node binding is recommended for speed in Node.js, which adds setup complexity
- The README is truncated and does not fully describe accuracy or limitations of each model
Getting started
deepface
Install from PyPI (ONNX Runtime backend by default)
pip install deepfaceInstall with TensorFlow backend
pip install "deepface[tensorflow]"Frequently asked questions
What is the difference between deepface and face-api.js?
deepface: Python library for face verification, face search, and estimating age, gender, emotion and race from photos. face-api.js: JavaScript library that detects faces and recognizes facial features in browsers and Node.js. deepface suits python developers prototyping face verification or face search, while face-api.js suits web developers adding face detection to a browser page.
Are deepface and face-api.js free?
deepface: Free and open source. face-api.js: Free and open source.
Is deepface or face-api.js more popular?
deepface has more GitHub stars (23.6k) than face-api.js (18k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to deepface and face-api.js?
Other options people compare them with include face_recognition, 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.