face-recognition
Recognize faces from Python or from the command line
Decision gist · record as of 2026-08-14
Yes, if you are on macOS or Linux and can install dlib. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and provides a straightforward API backed by a well-regarded deep learning model. Install friction is moderate due to dlib's compilation requirement, but the library itself installs cleanly. Not suitable for Windows without unofficial workarounds.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- dlib must be installed with Python bindings before installing face-recognition; requires macOS or Linux (Windows not officially supported).
- Low friction installation on macOS and Linux; depends on dlib, which requires compilation from source.
- Windows is not officially supported.
License · maintenance · safety
MIT license (permissive) — MIT license permits commercial and private use, modification, and distribution with minimal restrictions—suitable for most projects.
last release 2020-02-20 (2367 days) · last repo commit 2026-06-25 · 56,652 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 209,145 downloads/mo, #9,519 on PyPI
Alternatives
Verify before relying
pip install face-recognition
import face_recognition
image = face_recognition.load_image_file("photo.jpg")
face_locations = face_recognition.face_locations(image)- Whether the 99.38% accuracy figure on Labeled Faces in the Wild benchmark remains current for version 1.3.0.
- Real-world performance and accuracy on diverse face datasets outside the benchmark.
- GPU acceleration requirements and CUDA support status for deep-learning face detection model.
What it is and what it does
face-recognition wraps dlib's deep-learning face recognition model to provide a simple Python interface for detecting faces, extracting facial landmarks, and comparing face encodings to identify individuals. It includes both a Python API for programmatic use and a command-line tool for batch processing folders of images.
The library handles the core tasks of face detection (finding where faces appear in an image), facial feature location (eyes, nose, mouth, chin), and face identification (comparing unknown faces against known encodings to determine matches). It supports parallel processing across multiple CPU cores and allows tolerance tuning to adjust match sensitivity.
Use it for
- Batch identify people in a folder of photos by comparing against a reference set of known individuals.
- Extract and store face encodings from a database of known people for later comparison and matching.
- Detect all faces in an image and extract their landmark coordinates for facial feature analysis or digital effects.
- Build a real-time face recognition system by processing video frames with parallel CPU processing.
- Adjust recognition sensitivity by tuning tolerance values when similar-looking people cause false matches.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are on macOS or Linux and can install dlib.
The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and provides a straightforward API backed by a well-regarded deep learning model. Install friction is moderate due to dlib's compilation requirement, but the library itself installs cleanly. Not suitable for Windows without unofficial workarounds.
Install
face-recognition on PyPI
Before you install
Low friction installation on macOS and Linux; depends on dlib, which requires compilation from source. Windows is not officially supported. The package is actively maintained with recent commits, though the latest release is from 2020.
dlib must be installed with Python bindings before installing face-recognition; requires macOS or Linux (Windows not officially supported).
License in practice
MIT license permits commercial and private use, modification, and distribution with minimal restrictions—suitable for most projects.
Quickstart
pip install face-recognition
import face_recognition
image = face_recognition.load_image_file("photo.jpg")
face_locations = face_recognition.face_locations(image)Verify before relying
- Whether the 99.38% accuracy figure on Labeled Faces in the Wild benchmark remains current for version 1.3.0.
- Real-world performance and accuracy on diverse face datasets outside the benchmark.
- GPU acceleration requirements and CUDA support status for deep-learning face detection model.
Package facts
| License | MIT license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesface-recognition-modelsClickdlibnumpyPillow |
| Maintenance | Actively maintained 2,367 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 209,145 / month, #9,519 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishProgramming Language :: Python :: 2Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8 |
Evidence: face_recognition-1.3.0-py2.py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “identify people in photos”
- face-recognitionDetects, locates, and identifies faces in images using deep learning,…
- dbapiCommand-line client for interacting with Douban (豆瓣) social features…
- PyExifToolPyExifTool is a Python wrapper that communicates with Phil Harvey's…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also face_recognition_models · deepface · retina-face · face-alignment · dlib · mtcnn · insightface · facexlib · facenet-pytorch · retinaface-py