---
id: retina-face
version: "0.0.18"
license: unclear
license_treatment: permissive
maintenance: active
---

# retina-face — RetinaFace: Deep Face Detection Framework in TensorFlow for Python

License: permissive · Maintenance: active · Downloads: 219.1K/mo

## What it is and what it does

RetinaFace is a TensorFlow-based face detection library that identifies faces in images and returns their bounding boxes along with five facial landmarks (both eyes, nose, and mouth corners). It is designed as a preprocessing step for face recognition pipelines, where it can also align detected faces to improve downstream recognition accuracy. The package wraps a re-implementation of the original RetinaFace model from the insightface project, simplifying the API for pip installation while preserving the reference architecture and pre-trained weights.

The library exposes two main functions: detect_faces() returns face coordinates and landmarks with confidence scores, while extract_faces() crops and optionally aligns detected faces for use in recognition workflows. It depends on numpy, Pillow, opencv-python, and tensorflow, making it suitable for environments where those libraries are already present or acceptable as dependencies.

Use it for:

- Preprocessing images for a face recognition system by detecting and aligning faces before passing them to a recognition model.
- Extracting facial landmarks for face alignment or geometric analysis in computer vision applications.
- Building a face detection API or service that returns bounding boxes and confidence scores for detected faces in bulk image processing.
- Integrating face detection into a larger pipeline (e.g., with deepface) for end-to-end face verification or identification tasks.
- Analyzing crowd images to locate and extract individual faces for further processing or annotation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Detects faces in images and extracts facial landmarks (eyes, nose, mouth) using a deep learning model, with optional face alignment for recognition pipelines.

Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers low install friction. It is well-suited if you need face detection and landmark extraction as part of a Python computer vision workflow. Install it if you are building a face recognition pipeline or need facial landmarks for alignment; skip it if you only need bounding boxes and can tolerate heavier dependencies like tensorflow.

## Install

pip install retina-face
uv add retina-face
poetry add retina-face

## Installing retina-face

Before you install: Low install friction with a pure-Python wheel. Depends on numpy, Pillow, opencv-python, and tensorflow—all stable, widely-used libraries. Repository is active with a recent commit and no archived status.

License in practice: Licensed under MIT, a permissive license that allows commercial and private use with minimal restrictions.

Quickstart:

pip install retina-face

from retinaface import RetinaFace
resp = RetinaFace.detect_faces("img.jpg")
faces = RetinaFace.extract_faces(img_path="img.jpg", align=True)

Requires tensorflow and opencv-python, which may take time to install on first setup depending on system configuration.

Verify before relying:

- Whether pre-trained model weights are downloaded automatically on first use and where they are cached.
- Performance characteristics (inference time, memory usage) on typical hardware.
- Accuracy metrics on standard face detection benchmarks beyond the description's crowd-detection claim.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 219.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

face detection python, facial landmarks extraction, face alignment deep learning, detect faces in images, facial area coordinates, face recognition preprocessing, tensorflow face detector, face-detection, computer-vision, deep-learning

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