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How the game recognizes a move
- Capture an image. The input can be a still image, a decoded video frame, or a live camera frame. For interactive play, the program repeatedly reads frames from a camera.
- Find the hand. MediaPipe’s hand-landmark stage detects the hand and estimates its geometry. Google’s documentation describes 21 hand-knuckle coordinates and a landmark model trained on approximately 30,000 real-world images, in addition to rendered synthetic hand models. That figure describes landmark-model training data, not rock-paper-scissors accuracy. Google AI Edge’s Gesture Recognizer guide explains the task and its outputs.
- Classify the pose. A gesture classifier uses hand geometry to predict a category such as rock, paper, or scissors. The recognizer can also return a score, handedness, and landmarks; a predicted label is not a guarantee that the pose was recognized correctly.
- Apply the game rules. The game compares the recognized move with the other player’s move: matching moves tie; rock beats scissors, scissors beats paper, and paper beats rock. This rules step is ordinary program logic, independent of the vision model.
Keeping these stages separate makes errors easier to diagnose: a wrong label points to recognition or camera input, while a wrong winner points to the game’s comparison logic.
Choose an input and recognition approach
| Approach | What it involves | Best fit | Evidence and trade-off |
|---|---|---|---|
| Still-image recognition | Submit a saved image to the recognizer. | Trying out labels or checking sample poses without building a live camera loop. | Supported by the task guide; it does not by itself provide interactive play. |
| Live video with a pretrained recognizer | Send successive camera frames to MediaPipe’s Gesture Recognizer and use its predicted category in the game. | A quick prototype that uses a documented recognizer rather than training a new one. | MediaPipe documents live-video input. A public example combines webcam capture, MediaPipe processing, gesture classification, and an OpenCV display, but it is an implementation example, not a controlled accuracy test. See the NTU ARL RPS example. |
| Custom gesture model | Collect labeled images, train and evaluate a custom recognizer, then export a model asset bundle. | Adapting labels or examples to a particular game and its users. | Google documents a Model Maker workflow and an RPS sample dataset. Training does not guarantee reliable results from a small or unrepresentative dataset. See the customization guide. |
| Landmark geometry and hand-written rules | Track hand landmarks, then classify poses using geometric rules such as angles. | Experimenting with explicit, inspectable rules instead of a learned gesture classifier. | The cited repository demonstrates an angle-based approach. The available examples do not establish that it is more or less accurate than a learned classifier. |
For live play, the computer needs a camera, but it does not necessarily need a separate USB webcam: a usable built-in camera can supply the frames. The cited example opens the default webcam; it does not recommend particular camera models.
Build a recognizer that can reject unclear poses
Start with the available categories
MediaPipe’s customization guide includes a rock-paper-scissors sample with four labels: rock, paper, scissors, and none. The none category represents gestures outside the named game moves. It gives the game a way to avoid turning every visible hand pose into a valid throw.
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Train and evaluate custom labels
The documented customization workflow organizes training images in folders by label and includes none examples. It also describes running the prepackaged hand detector to locate landmarks before training the gesture model. In practical terms, the first stage supplies hand geometry; the second learns which target category that geometry represents.
Google’s developer blog illustrates the broader Model Maker sequence: load and split data, train a custom recognizer, evaluate it on test data, and export a model asset bundle. The blog presents RPS as an example of customization, not a promise that any particular dataset will perform well. Read Google’s MediaPipe Solutions overview.
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Use confidence and game state deliberately
The task guide documents score thresholds and hand-presence confidence settings. Use them, along with an unknown or none result, so the game can wait for a clearer pose instead of treating a weak prediction as a confirmed move. The right thresholds depend on the application and should be tested with its camera and users.
A live game should also define when a throw counts. For example, it can wait until a hand is present and the predicted category clears the chosen threshold, then register one move and wait for the next round. That prevents a stream of nearly identical frames from counting as several throws. The recognizer supplies predictions; round timing and move locking belong to the game logic.
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Test the conditions players will actually use
There is no established general accuracy percentage for this game across lighting, backgrounds, users, cameras, or devices. Results can be affected by image quality, lighting, hand occlusion, camera framing, and whether training examples represent the poses the system will encounter. These are practical factors to test, not quantified performance claims from the cited examples.
- Try each move with multiple users and hand positions, including poses that should be rejected as
none. - Vary the lighting, background, distance, and camera framing expected during play.
- Check the prediction and score before debugging the win/tie/loss rules.
- If publishing an accuracy figure, state the test set, users, camera, hardware, lighting, and evaluation method. Do not present the approximately 30,000-image landmark-training figure as a game-accuracy result.
A 2025 IEEE conference abstract describes a rock-paper-scissors implementation using MediaPipe, OpenCV, and camera video, but an implementation description is not an independent performance validation. See the IEEE Xplore abstract.
Quick Recap
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