Practical overview
When it fits
Use Libfaceid when learning how face-recognition components fit together or when prototyping model combinations and comparing their system trade-offs. Its README also documents a dataset-to-training-to-image-testing workflow.
Who it is for
- Python developers prototyping face-recognition pipelines.
- Learners comparing face-detection and embedding approaches.
- Researchers experimenting with liveness detection or combinations of facial analysis and speech features.
Documented uses
- Compare and mix face detectors and embedding models within a recognition prototype.
- Organize a dataset, train a selected model and test it against an image using the documented scripts.
- Explore liveness detection and related facial estimates such as age, gender, emotion and landmarks.
- Add optional speech recognition or synthesis to voice-activated or voice-enabled experiments.
Requirements & limits
The public GitHub repository lists an MIT license. The README specifies Python 3 and pip and documents older dependency versions.
- Model choices differ in speed, accuracy, memory requirements and third-party dependencies.
- The README pins an older software stack, including TensorFlow 1.8.0, Keras 2.0.8 and OpenCV 3.4.3.18.
- Speech capabilities use a separate requirements file and require additional platform-specific packages.
- The README describes some Flask test applications for viewing video capture from another computer on the same network.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install the documented core dependencies
Prepare a Python environment with the dependencies listed for the library.
Before you start
- Python 3 and Python pip.
- The repository's requirements.txt file.
- CMake for dlib, or the listed Windows wheel option.
Steps
Install Python 3 and pip.
Install the dependencies with pip install -r requirements.txt.
If installing dlib, install CMake or use the wheel option described in the README.
Expected outcome
The core Python dependencies listed by the README are installed.
Guide limitations
- The README gives Python 3.5.3 as its Raspberry Pi 3B+ example and Python 3.6.6 for Windows.
- The pinned dependency versions are old; the guide does not establish compatibility with current Python environments.
Train and test a face-recognition model with the included scripts
Follow the README's documented dataset, training and image-testing sequence.
Before you start
- The library and its dependencies.
- A dataset organized in person-specific folders, with example paths such as datasets/person1/1.jpg and datasets/person2/1.jpg.
Steps
Add face images to person-specific dataset folders.
Edit training.bat to specify the chosen models.
Run training.bat.
Edit testing_image.bat to specify the chosen models, then run it.
Expected outcome
The documented workflow trains a model from the dataset and proceeds to image testing.
Guide limitations
- The README calls this a dummy quickstart and does not specify dataset size or evaluation criteria.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- richmondu/libfaceid · Official homepage title · retained in its source language