NeuroLip: An Event-driven Spatiotemporal Learning Framework for Cross-Scene Lip-Motion-based Visual Speaker Recognition

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

NeuroLip is an event-based framework for visual speaker recognition (VSR) that leverages fine-grained lip motion dynamics to achieve robust identification across varying environmental conditions. Developed by Junguang Yao, Wenye Liu, Stjepan Picek, and Yue Zheng, this system addresses the challenge of cross-scene generalization, where training occurs under a single controlled condition (e.g., frontal view, standard illumination) but recognition must extend to unseen viewpoints and lighting. NeuroLip integrates three key modules: Temporal-aware Voxel Encoding (TVE) for adaptive event weighting, a Structure-aware Spatial Enhancer (SSE) to amplify discriminative patterns, and Polarity Consistency Regularization (PCR) to preserve motion-direction cues. To facilitate evaluation, the researchers also introduced DVSpeaker, a new event-based lip-motion dataset featuring 50 subjects recorded under four distinct viewpoint and illumination scenarios. Experiments show NeuroLip achieves 100% accuracy in matched scenes and over 71% accuracy on unseen viewpoints and nearly 76% under low-light conditions, outperforming 23 existing methods by at least 8.54%.

Key takeaway

For research scientists developing biometric systems, NeuroLip demonstrates that event-based vision significantly enhances cross-scene generalization for lip-motion-based speaker recognition. You should consider integrating event cameras and similar spatiotemporal processing pipelines, especially for applications requiring robust identification under diverse and unpredictable environmental conditions, such as varying viewpoints and illumination. This approach offers a path to more secure and adaptable biometric solutions.

Key insights

Event cameras and specialized processing can enable robust lip-motion-based speaker recognition across diverse scenes.

Principles

• Lip motion provides stable, behavior-driven biometric cues.
• Event cameras excel at capturing fine-grained motion dynamics.
• Polarity information in event streams encodes crucial motion direction.

Method

NeuroLip preprocesses event streams, then uses Temporal-aware Voxel Encoding, a Structure-aware Spatial Enhancer, and Polarity Consistency Regularization to extract and classify robust lip-motion features for cross-scene visual speaker recognition.

In practice

• Use event cameras for robust biometrics in varied light.
• Employ adaptive temporal encoding for sparse event data.
• Regularize models to preserve event polarity for motion cues.

Topics

• Visual Speaker Recognition
• Event Cameras
• Cross-Scene Generalization
• Lip Motion Biometrics
• NeuroLip Framework

Code references

• JiuZeongit/NeuroLip

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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• NeuroLip: An Event-driven Spatiotemporal Learning Framework for Cross-Scene Lip-Motion-based Visual Speaker Recognition — Event-based sensing and spatiotemporal learning enhance lip-motion biometrics for robust cross-scene speaker recognition.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.