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Faceimagequality

Faceimagequality is a Python research implementation of SER-FIQ, an unsupervised method that estimates face-image quality from the robustness of embeddings produced by stochastic variations of a face-recognition model. Its included implementation uses ArcFace and outputs normalized quality values.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Faceimagequality — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a research implementation for assessing face-image quality alongside a face-recognition system, and can work with its Python dependencies and model files.

Who it is for

  • Researchers prototyping face-image quality assessment.
  • Developers evaluating image suitability within a face-recognition workflow.

Documented uses

  • Estimate face-image suitability for recognition without labeled quality scores.
  • Rank or filter face images using normalized quality values.
  • Explore quality assessment using an ArcFace-based SER-FIQ implementation.
Before you choose

Requirements & limits

  • The included code is an ArcFace-based demonstration of the SER-FIQ concept.
  • The repository recommends a face-recognition network trained with dropout.
  • The assessment may transfer bias from the underlying recognition system.
  • Python 3.7 or 3.8 is recommended, and model files must be downloaded separately.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the SER-FIQ example prerequisites

Prepare the repository’s Python environment and model files for its included example.

Before you start

  • Python 3.7 or 3.8 is recommended.
  • A virtual environment is recommended.
  • The repository’s requirements file, or the listed manual packages.
  • The downloaded model files.

Steps

  1. Create and activate a Python virtual environment.

  2. Install dependencies with the command below, or install the listed packages manually, replacing mxnet-cuXYZ with the CUDA version.

    Documented command
    pip install -r requirements.txt

  3. Download and extract the model files into the insightface/model folder.

Expected outcome

The environment and model files are prepared for the repository’s example script.

Guide limitations

  • These setup instructions do not describe broader deployment or integration steps.
  • The repository specifies a CUDA-version substitution for the manual MXNet package.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
pterhoer/FaceImageQuality · Official homepage title · retained in its source language
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