用手机摄像头和指纹融合实现更精准的用户认证
Multimodal Biometric Authentication Using Camera-Based PPG and Fingerprint Fusion
- 用双模态状态空间模型处理视频脉搏信号与指纹图像
- 在单次和多次会话中均实现高准确率验证
- 适合需要高安全性的移动端生物识别场景
基于智能手机摄像头的摄影容积描记法（PPG）在个性化健康监测与安全认证方面展现出巨大潜力。本文提出一种多模态生物识别系统，将视频中提取的PPG信号与指纹数据融合，以提升用户身份验证的准确性。用户只需将指尖置于摄像头镜头上数秒，即可完成生物特征采集与处理。系统采用包含两个结构化状态空间模型（SSM）编码器的神经网络，将指纹图像转换为像素序列，并与分段的PPG波形一同输入编码器。通过跨模态注意力机制提取精炼特征表示，并使用面向分布的对比损失函数，在统一潜在空间中对齐特征。实验结果表明，该系统在单次会话和双次会话认证场景下，各项评估指标均表现优异。
原文摘要 · Abstract (English)
Camera-based photoplethysmography (PPG) obtained from smartphones has shown great promise for personalized healthcare and secure authentication. This paper presents a multimodal biometric system that integrates PPG signals extracted from videos with fingerprint data to enhance the accuracy of user verification. The system requires users to place their fingertip on the camera lens for a few seconds, allowing the capture and processing of unique biometric characteristics. Our approach employs a neural network with two structured state-space model (SSM) encoders to manage the distinct modalities. Fingerprint images are transformed into pixel sequences, and along with segmented PPG waveforms, they are input into the encoders. A cross-modal attention mechanism then extracts refined feature representations, and a distribution-oriented contrastive loss function aligns these features within a unified latent space. Experimental results demonstrate the system's superior performance across various evaluation metrics in both single-session and dual-session authentication scenarios.
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