Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint

A multimodal biometric system integrates information from more than one biometric modality to improve the performance of each individual biometric system and make the system robust to spoof attacks. In this paper, we propose a secure multimodal biometric system that uses convolution neural network (CNN) and Q-Gaussian multi support vector machine (QG-MSVM) based on a different level fusion. We developed two authentication systems with two different level fusion algorithms: a feature level fusion and a decision level fusion. The feature extraction for individual modalities is performed using CNN. In this step, we selected two layers from CNN that achieved the highest accuracy, in which each layer is regarded as separated feature descriptors. After that, we combined them using the proposed internal fusion to generate the biometric templates. In the next step, we applied one of the cancelable biometric techniques to protect these templates and increase the security of the proposed system. In the authentication stage, we applied QG-MSVM as a classifier for authentication to improve the performance. Our systems were tested on several publicly available databases for ECG and fingerprint. The experimental results show that the proposed multimodal systems are efficient, robust, and reliable than existing multimodal authentication systems.