Advanced Gesture Recognition in Gaming: Implementing EfficientNetV2-B1 for "Rock, Paper, Scissors"
Received: 29 January 2025 | Revised: 27 February 2025 | Accepted: 6 March 2025 | Online: 6 May 2025
Corresponding author: Biswaranjan Acharya
Abstract
The study introduces a gesture recognition system for the classic "Rock, Paper, Scissors" game, based on a modified EfficientNetV2-B1 architecture. The dataset comprises 2,700 images, evenly divided among the three classes: "Rock", "Paper", and "Scissors". Leveraging the efficiency and accuracy of the EfficientNetV2-B1 model in image recognition tasks, the system was trained to classify these gestures effectively, and after fine-tuning, it achieved an accuracy of 98.89% and an Area Under the Curve (AUC) of ~1.0, indicating near-perfect classification across all classes. This performance highlights the potential of EfficientNetV2-B1 for real-time gesture recognition, with applications in interactive gaming and other gesture-based user interfaces. The proposed system also offers a foundation for further research and development in gesture recognition technologies.
Keywords:
rock-paper-scissors, deep learning, EfficientNetV2-B1, gesture recognitionDownloads
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Copyright (c) 2025 Chander Prabha, Retinderdeep Singh, Meena Malik, Manas Ranjan Pradhan, Biswaranjan Acharya
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