PlaySense-ID: Haptic-Enhanced Multimodal Biometric Authentication for Gaming Controller Users

In this work, we propose a multimodal biometric authentication system leveraging sig nals from inertial sensors and audio-based haptic responses collected through standard gaming controllers. To enhance the biometric representation, we introduce a haptic-enhanced chirp signal and design a feature fusion strategy that integrates inertial and MFCC-based audio features. Our approach employs a novel LSTM-based architecture with multi-head self-attention, dual cross-modal attention, dynamic feature fusion, and residual correction, significantly improving model performance over traditional method without haptic input. Experimental evaluations demonstrate that the proposed method achieves a substantial improvement in authentication accuracy, achieving an accuracy of 98.5% and reducing Equal Error Rate (EER) to 0.4%. Robustness and generalization tests, conducted under varying environmental conditions and background noise scenarios, further validate the model’s resilience to real-world disturbances. Moreover, the proposed method provides practical advantages for deployment, offering a seamless, low-cost, and non-intrusive authentication experience suitable for immersive gaming environments.