Identification based on feature fusion of multimodal biometrics and deep learning

This paper proposes a novel methodology for individuals identification based on convolutional neural network (CNN) and machine learning (ML) algorithms. The technique is based on fusioning biometric modalities at the feature level. For this purpose, several hybrid multimodal-biometric systems are used as a benchmark to measure accuracy of identification. In these systems, a CNN is used for each modality to extract modality-specific features for pattern of datasets. Machine learning algorithms are used to identify (classify) individuals. In this paper, we emphasise on performing fusion of biometric modalities at the feature level. We propose to apply the proposed algorithms on two challenging databases: FEI face database and IITD Palm Print V1 dataset. The results are showing good accuracies with many proposed multimodal biometric person identification systems. Through experimental runs on several multi-modal systems, it is clearly shown that best identification performance is obtained when using ResNet18 as deep learning tools for feature extraction along with linear discrimination machine learning algorithm.