NeuroLip Framework Uses Event-Based Cameras for Visual Speaker Recognition via Lip Motion
A new framework called NeuroLip has been developed to address visual speaker recognition through lip motion analysis. This approach provides a biometric solution that functions without sound and remains effective when audio cues are absent. Unlike traditional methods dependent on appearance, lip motion captures behavioral dynamics driven by consistent articulation patterns and muscle coordination. These dynamics offer inherent stability across different environmental conditions. However, capturing fine-grained lip movements proves challenging for conventional frame-based cameras due to issues like motion blur and limited dynamic range. To overcome these sensing limitations and exploit the stability of lip motion, NeuroLip employs event-based technology. The framework operates under a strict cross-scene protocol where training occurs in a single controlled setting. Recognition must then generalize to unseen viewing angles and lighting conditions. This research was announced on arXiv with the identifier 2604.15718v1.
Key facts
- NeuroLip is an event-based framework for visual speaker recognition.
- It analyzes lip motion as a silent, hands-free biometric solution.
- Lip motion encodes subject-specific behavioral dynamics from articulation patterns and muscle coordination.
- This method offers inherent stability across environmental changes compared to appearance-dependent approaches.
- Conventional frame-based cameras struggle with fine-grained lip dynamics due to motion blur and low dynamic range.
- The framework uses a cross-scene protocol: training under a single controlled condition, recognition in unseen conditions.
- The research was announced on arXiv with the identifier 2604.15718v1.
- Visual speaker recognition remains effective when acoustic cues are unavailable.
Entities
Institutions
- arXiv