facemind

What facemind does

Facemind is an application that applies computer vision and machine learning to estimate mental health signals from a person's facial expressions. The README states that the app includes a login system and performs real time mental health analysis through facial landmarks, which means it tracks specific points on the face as the user is seen by a camera and derives indicators from how those points move. The project is built around the idea that expression patterns can be captured and processed on a local machine rather than sent to a generic cloud service. It is published as a Python package named mental_health_app on PyPI, so it can be installed like any other library, and it also ships as a container image at ghcr.io/galihru/mental_health_app for users who prefer Docker. The repository credits two authors, Ana Maulida and GALIH RIDHO UTOMO, and describes the work as a delegation for a competition paper, which explains the academic framing and the focus on a self contained demonstration. With 37 stars the project is small, but it packages a complete pipeline from camera capture to analysis. Anyone exploring affective computing or student projects on expression based inference will find a concrete, runnable example here rather than only a description of the method.

How the analysis works

The analysis pipeline relies on a familiar set of Python libraries for vision and interfaces. OpenCV, through the opencv-python package, handles camera input and image processing. Mediapipe provides the facial landmark model that locates the main points on a face, which is the input the mental health estimation builds on. PyQt5 supplies the desktop application window and controls, including the login screen mentioned in the README. The combination means the heavy lifting of landmark detection is done by a mature model while the application logic and user interface are written in Python with a standard GUI toolkit. Selenium appears in the dependency list alongside pandas, which suggests the project also includes some browser driven or data handling steps beyond the core camera loop, though the README does not detail those parts. The real time nature comes from repeatedly reading landmarks and updating the analysis as the session continues. Because the README keeps the explanation brief, the exact scoring method is not spelled out, and readers should treat the output as an experimental signal rather than a clinical measurement. The value of the repository is that it wires together camera, landmarks, and a GUI into one runnable program that a student or researcher can study and extend.

Setup and dependencies

Getting facemind running starts with installing its Python dependencies. The README lists opencv-python, numpy, mediapipe, selenium, PyQt5, and pandas as the required packages, and it points users to a Docker image as the fastest path: the command docker pull ghcr.io/galihru/mental_health_app:latest fetches a prebuilt image so the environment is already assembled. For a manual setup, the same list of packages would be installed into a Python environment before launching the application. The project is licensed under the Apache 2.0 license, which permits use, modification, and redistribution with attribution, fitting its role as a competition and educational artifact. The repository metadata lists TeX as the primary language, which is unusual for a Python application and reflects the accompanying paper and documentation written in TeX rather than the application code itself. The homepage is hosted at galihru.github.io/facemind, where the project presents its logo and summary. Because the README is short, new users should expect to read the source for the precise launch steps and to confirm camera permissions on their platform. The packaging as both a PyPI library and a container keeps the barrier to trying it low for anyone curious about expression based mental health tooling. The README's brevity means the source remains the best place to learn the exact launch steps and camera permissions.

Editorial conclusion

Facemind is released under the Apache 2.0 license and is published on PyPI as the mental_health_app package, created by Ana Maulida and GALIH RIDHO UTOMO for a competition paper.