Hybrid Score- and Rank-level Fusion for Person Identification using Face and ECG Data
This work addresses identification errors in biometric systems for security or healthcare applications, but it is incremental as it combines existing modalities.
The paper tackled the problem of person identification by fusing face and ECG data to reduce errors from uni-modal systems, achieving an accuracy of 99.8% compared to 98.8% for face and 96.1% for ECG alone.
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.