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Table 4 Comparison between feature-level fusion and score-level fusion

From: Deep user identification model with multiple biometric data

Fusion Level Rule Weight Accuracy (%)
   ECG Face Finger ID Gender
Feature - - - - 98.97 96.55
Score Sum 0.33 0.33 0.33 98.27 99.42
   0.50 0.25 0.25 98.85 99.42
   0.25 0.50 0.25 98.85 99.42
   0.25 0.25 0.50 97.70 99.42
Score Product 0.33 0.33 0.33 96.55 89.08
   0.50 0.25 0.25 95.98 89.66
   0.25 0.50 0.25 93.10 89.08
   0.25 0.25 0.50 93.68 87.36
Score Max 0.33 0.33 0.33 89.66 89.66
   0.50 0.25 0.25 89.66 87.93
   0.25 0.50 0.25 89.66 86.21
   0.25 0.25 0.50 89.66 87.36
  1. From this experiment, the feature fusion of three modalities shows the best performance in user identification task. For gender classification task, the score-level fusion shows the best performance regardless of weights in each physiological data. This result shows that feature-level fusion method shows good performance even without adjusting weight