NeuroLip: An Event-driven Spatiotemporal Learning Framework for Cross-Scene Lip-Motion-based Visual Speaker Recognition
Summary: arXiv:2604.15718v1 Announce Type: cross
Introduction
Visual speaker recognition based on lip motion has emerged as a promising biometric solution that is silent, hands-free, and behavior-driven. This technique remains effective even in situations where acoustic cues are absent. Unlike traditional methods that depend heavily on appearance-based representations, lip motion provides insights into subject-specific behavioral dynamics characterized by consistent articulation patterns and muscle coordination. This intrinsic stability allows for effective recognition across varying environmental conditions.
Challenges in Traditional Methods
Despite the potential of lip motion for speaker recognition, capturing fine-grained dynamics poses significant challenges. Conventional frame-based cameras often struggle with motion blur and limited dynamic range, leading to difficulties in accurately interpreting lip movements. These limitations necessitate the development of more advanced frameworks that can leverage the stability of lip motion while overcoming the constraints of traditional imaging techniques.
Introducing NeuroLip
To address these challenges, we introduce NeuroLip, an innovative event-based framework designed to capture fine-grained lip dynamics effectively. NeuroLip operates under a strict yet practical cross-scene protocol, where training occurs in a controlled environment, and recognition must generalize to unseen viewing angles and lighting conditions.
Key Features of NeuroLip
- Temporal-aware Voxel Encoding Module: This module utilizes adaptive event weighting to enhance the representation of lip movements over time.
- Structure-aware Spatial Enhancer: This feature amplifies discriminative behavioral patterns while suppressing noise, ensuring that vertically structured motion information is preserved.
- Polarity Consistency Regularization Mechanism: This mechanism is crucial for retaining motion-direction cues encoded in event polarities, which are essential for accurate recognition.
DVSpeaker Dataset
To facilitate a systematic evaluation of NeuroLip, we introduce DVSpeaker, a comprehensive event-based lip-motion dataset comprising recordings of 50 subjects. This dataset was captured under four distinct viewpoints and varying illumination scenarios, providing a robust foundation for testing the framework’s generalization capabilities.
Experimental Results
Extensive experiments have demonstrated that NeuroLip achieves near-perfect matched-scene accuracy. Furthermore, it exhibits robust cross-scene generalization, attaining over 71% accuracy on unseen viewpoints and nearly 76% under low-light conditions. These results indicate that NeuroLip significantly outperforms existing representative methods by at least 8.54%.
Conclusion and Availability
The introduction of NeuroLip marks a significant advancement in the field of visual speaker recognition, effectively utilizing lip motion to enhance biometric identification. The dataset and code related to this research are publicly available at https://github.com/JiuZeongit/NeuroLip, encouraging further exploration and development in this promising area of study.