Ensemble of convolutional neural network and DeepResNet for multimodal biometric authentication system

Multimodal biometrics technology has garnered attention recently for its ability to address inherent limitations found in single biometric modalities and to enhance overall recognition rates. A typical biometric recognition system comprises sensing, feature extraction, and matching modules. The system’s robustness heavily relies on its capability to effectively extract pertinent information from individual biometric traits. This study introduces a novel feature extraction technique tailored for a multimodal biometric system utilizing electrocardiogram (ECG) and iris traits. The ECG helps to incorporate the liveliness related information and Iris helps to produce the unique pattern for each individual. Therefore, this work presents a multimodal authentication system where data pre-processing is performed on image and ECG data where noise removal and quality enhancement tasks are performed. Later, feature extraction is carried out for ECG signals by estimating the Heart rate variability feature analysis in time and frequency domain. Finally, the ensemble of convolution neural network (CNN) and DeepResNet models are used to perform the classification. The overall accuracy is reported as 0.8900, 0.8400, 0.7900, 0.8932, 0.87, and 0.97 by using convolutional neural network-long short-term memory (CNN-LSTM), support vector machine (SVM), random forest (RF), CNN, decision tree (DT), and proposed MBANet approach respectively.