DeepFace
The Swiss-army-knife Python library for face recognition: verify, find, and analyze age, gender, emotion, and race in a few lines of code.
About
DeepFace (the Python library, not Facebook's 2014 model of the same name) wraps a whole zoo of recognition models — VGG-Face, FaceNet (128d and 512d), OpenFace, DeepID, ArcFace, Dlib, SFace, GhostFaceNet, and Buffalo_L — behind one API. It adds face detection (OpenCV, SSD, MTCNN, RetinaFace, MediaPipe, YuNet, YOLO, CenterFace), a face database search function, facial attribute analysis, and optional anti-spoofing.
It is easily the most approachable way to ship face recognition in a Python app: pip install deepface and you get verification, recognition, and attribute analysis without training anything. The catch is license inheritance — the library itself is MIT, but each wrapped model carries its own license, so production use needs a model-by-model check. A managed hosted API exists at deepface.dev, but it is a separate commercial product.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
No published independent benchmarks found for the library as a whole. Its docs cite published numbers showing the wrapped models passing human-level 97.53% face-recognition accuracy; those figures belong to the underlying models, not to this library's integration.
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 8/10
Scored 8/10 (verified, metrics-v1). Evidence: peer-reviewed papers (LightFace, ASYU 2020; follow-up in Gazi University Journal of Science 2026) by the named author; MIT license on the library; very large community adoption and active maintenance through 2026. Strengths: genuinely open, one API over many state-of-the-art models, active repo. Docked for: (1) wrapped third-party models each carry their own licenses, complicating commercial use; (2) no independent benchmark of the library as an integrated system. License: MIT (library); model licenses inherited per model.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 10.0/10
- Maintenance · recency, releases, issue load
- 9.0/10
- Openness · license + docs
- 8.0/10
- Manual review · sanity read + red-flag check
- 8.0/10
- · serengil/deepface; peer-reviewed LightFace papers (ASYU 2020, GUJS 2026).
- · Wraps VGG-Face, FaceNet, OpenFace, DeepID, ArcFace, Dlib, SFace, GhostFaceNet, Buffalo_L plus many detectors and optional anti-spoofing.
- · Managed deepface.dev API is a separate commercial product, not the library.