Transfer learning convolutional neural network with modified Lion optimization for multimodal biometric system

Recently, biometric verification systems have been widely used in security applications with multi-level authentication. Existing machine learning approaches were utilized to develop traditional multimodal biometric systems (MBS). However, they fail to guarantee optimal secrecy, security, and accuracy. Thus, this work adopted the transfer learning convolutional neural network (TL-CNN) approach for implementing the eight-class hybrid MBS. The multi-level security biometric verification systems are achieved by considering eight different types of biometric datasets, which are the retina, faces, ears, palm print, fingerprint, voice, gait, and DNA-based biometric data. Initially, the noise from these datasets is eliminated by using Multi-Kernel-Multi-Patch Bilateral Filtering (MK-MP-BF), which also enhances the regions of images. Further, a deep convolutional residual network (DCRN) is used to extract pattern-specific features from the pre-processed data, which also identifies the relationship between various features. Then, a bio-optimization-based modified Lion optimization algorithm (MLOA) is used to select the optimal feature, which also identifies the inter- and intra-dependencies among various biometrics. Finally, a TL-CNN-based GoogleNet classifier was utilized for recognizing the biometrics, which also performed the multi-class classification operation. From the simulations, it is proven that the proposed MLOA optimized TL-CNN resulted in outstanding recognition and authentication performance in comparison with conventional methods.