Abstract
Pigo is an open-source AI & Machine Learning project. Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go. The reason why Pigo has been developed is because almost all of the currently existing solutions for face detection in the Go ecosystem are purely bindings to some C/C++ libraries like OpenCV or dlib, but calling a C program through cgo introduces huge latencies and implies a significant trade-off in terms of performance. Also, in many cases installing OpenCV on various platforms is cumbersome. It is built using Go, Computer Vision, Machine Learning, OpenCV. Key capabilities include: Does not require OpenCV or any 3rd party modules to be installed; High processing speed; There is no need for image preprocessing prior to detection. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
The reason why Pigo has been developed is because almost all of the currently existing solutions for face detection in the Go ecosystem are purely bindings to some C/C++ libraries like OpenCV or dlib, but calling a C program through cgo introduces huge latencies and implies a significant trade-off in terms of performance. Also, in many cases installing OpenCV on various platforms is cumbersome.
2. Objective
Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go.
This project demonstrates how Go, Computer Vision, Machine Learning can be applied to a real-world AI & Machine Learning problem.
3. Key Features / Modules
- Does not require OpenCV or any 3rd party modules to be installed
- High processing speed
- There is no need for image preprocessing prior to detection
- There is no need for the computation of integral images, image pyramid, HOG pyramid or any other similar data structure
- The face detection is based on pixel intensity comparison encoded in the binary file tree structure
- Fast detection of in-plane rotated faces
- The library can detect even faces with eyeglasses
- Pupils/eyes localization
- Facial landmark points detection
- Webassembly support
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Go 1.20 or later
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/esimov/pigo.git
cd pigo$ go install github.com/esimov/pigo/cmd/pigo@latest$ pigo -in input.jpg -out out.jpg -cf cascade/facefinderFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by esimov and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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