论文标题
使用面部和ECG数据的人识别的混合评分和等级融合
Hybrid Score- and Rank-level Fusion for Person Identification using Face and ECG Data
论文作者
论文摘要
Uni-Modal标识系统容易受到传感器数据收集错误的影响，因此更有可能误导受试者。例如，仅靠RGB面相机依靠数据可能会在光线较差的环境中引起问题，或者如果受试者不面对相机。其他识别方法（例如心电图）（ECG）存在与皮肤铅连接不当的问题。通过从这两个模型中收集的信息融合来融合识别错误。本文提出了一种使用包含同步RGB-VIDEO的Biovid Heat Pain数据库的A部分组合面部和ECG数据的识别结果的方法，以及87名受试者的ECG数据。使用10倍的交叉验证，面部识别精度为98.8％，而心电图鉴定精度为96.1％。通过使用融合方法，识别精度提高到99.8％。我们提出的方法可以通过使用具有非重叠方式的不同面部和ECG模型来显着提高识别精度。
Uni-modal identification systems are vulnerable to errors in sensor data collection and are therefore more likely to misidentify subjects. For instance, relying on data solely from an RGB face camera can cause problems in poorly lit environments or if subjects do not face the camera. Other identification methods such as electrocardiograms (ECG) have issues with improper lead connections to the skin. Errors in identification are minimized through the fusion of information gathered from both of these models. This paper proposes a methodology for combining the identification results of face and ECG data using Part A of the BioVid Heat Pain Database containing synchronized RGB-video and ECG data on 87 subjects. Using 10-fold cross-validation, face identification was 98.8% accurate, while the ECG identification was 96.1% accurate. By using a fusion approach the identification accuracy improved to 99.8%. Our proposed methodology allows for identification accuracies to be significantly improved by using disparate face and ECG models that have non-overlapping modalities.
