A Multi-Biometric System Based on Multi-Level Hybrid Feature Fusion

In a multimodal biometric recognition system, the integration of multiple features derived from various biometric modalities seeks to overcome the several drawbacks found in a unimodal biometric system. In this paper, we have proposed a novel multimodal biometric recognition system based on a multi-level hybrid feature fusion mechanism to compact knowledge from multiple feature vectors. Several pre-trained networks with transfer learning, namely AlexNet, Inceptionv2, Densenet201, Resnet101, and Resnet-Inceptionv2, are employed to extract feature vectors to fuse with handcrafted feature vectors based on HOG feature descriptor. Canonical correlation analysis (CCA) and Discriminant Correlation Analysis (DCA) are utilized at a multi-level hybrid mechanism. To test the proposed framework, we used three biometric features: Ear, Face, and Gait. Numerical results have proved that our model outperformed other state of the art recent variants.