Sign Language Interface with MediaPipe

3/24/2026

This post introduces the integration of Gemini with MediaPipe to enable developers to build highly interactive games and apps using natural language prompts in Google AI Studio. It showcases examples like a motion-controlled Chrome Dino game using MediaPipe Pose Landmarker, a hair recoloring app using Image Segmenter, and various games leveraging face landmarks, gesture recognition, and face detection for real-time on-device processing. It also highlights the upgrade of MediaPipe face detection to support long-range distances.

5/14/2024

Project Gameface has been launched on Android, making its hands-free cursor control technology available for Android applications. This involves using the Android Accessibility Service to create a virtual cursor and leveraging MediaPipe's Face Landmarks Detection API, specifically its 52 face blendshape values, to map facial expressions and head movements to cursor control and actions. The implementation supports mobile actions like GLOBAL_ACTION_HOME, GLOBAL_ACTION_BACK, GLOBAL_ACTION_NOTIFICATIONS, and GLOBAL_ACTION_ACCESSIBILITY_ALL_APPS. A drag function has been implemented using the Android Accessibility Service to allow users to define start and end points for dragging actions. The camera feed is integrated to provide user feedback and maintain overlay visibility across the Android OS.

1/18/2024

This post details how HealthPulse AI, a digital health non-profit, leverages MediaPipe to improve the accuracy and accessibility of rapid diagnostic tests (RDTs) for diseases like HIV, COVID-19, and malaria. It highlights the use of MediaPipe's platform-agnostic, efficient inference engine to run computer vision and machine learning algorithms locally on low-end smartphones, addressing challenges of unreliable internet connectivity and diverse mobile platforms. The HealthPulse AI pipeline uses MediaPipe calculators for image quality assurance (IQA) and RDT classification, enabling real-time feedback to users to retake photos if quality is insufficient. This contributes to increased health equity by democratizing access to timely, quality care in underserved communities.

10/5/2023

This post provides seven dos and don'ts for web developers using MediaPipe Solutions for machine learning. Key recommendations include not bundling ML models within the app but storing them in cloud storage and caching them locally, initializing tasks early to hide processing from the user, cleaning up WebAssembly resources using the .close() method, utilizing MediaPipe Studio for testing and customization, testing on different devices, and avoiding defaulting to the largest model, prioritizing speed and size based on use case.

6/12/2023

Introduced Project Gameface, an open-source, hands-free gaming mouse utilizing MediaPipe's Face Landmarks Detection API with blendshape output. Detailed the use of forehead landmarks for cursor movement, separate cursor speed adjustment for different directions, and adjustable cursor smoothing to combat jitter. Explained the customization of facial expressions for mouse actions (click, scroll, reset cursor) and keyboard presses. Described the gesture size feature for triggering actions based on expression intensity. Highlighted the use of PyAutoGUI for mouse control, PyDirectInput for keyboard control, PyInstaller for packaging, and CustomTkinter for the UI.

9/13/2021

This post details how MediaPipe Hands, a machine learning solution for hand and finger tracking, can be used to control drones. It describes the technical implementation of using hand gestures detected by MediaPipe to send commands to a drone, enabling gesture-based control for applications like aerial photography and remote sensing. The post highlights the real-time processing capabilities of MediaPipe and its potential for creating intuitive human-computer interfaces for drone operation.

4/14/2021

This post announces the release of the SignAll SDK, which integrates MediaPipe for sign language recognition. It provides developers with the tools to build applications that can interpret sign language, focusing on the technical implementation of gesture recognition and its application in software development.