Multimodal Biometrics Authentication in Healthcare Using Improved Convolution Deep Learning Model

Abstract

In healthcare applications, biometric authentication is crucial in managing patient credential details. The limited usage of biometric traits causes personal details theft, treatment hacking, and payment hijacking. Multimodal biometrics should incorporate into the healthcare system to maintain security and privacy in healthcare applications. Previous methods ensure authentication and security consume high computation complexity and fail to maintain system reliability and scalability. Therefore, in this work, an improved convolution deep learning model (ICDLM) is applied to manage the patient traits in the healthcare center. Initially, Experimentation is conducted with SWAN DATASET, which consists of face, voice, and periocular biometric features. The biometric characteristics are investigated according to the super point convolution neural model. The model analyzes the trait descriptors and interest points to identify the patterns. The extracted patterns are utilized to match the patient while accessing the information in healthcare applications. This process uses template pattern matching (TPM), which minimizes the absolute difference while authenticating the users. The system’s efficiency implemented using Python and excellency is compared with traditional approaches. The system attains 98.31% and 98.67% accuracy on the number of users and attempts with a minimum false identification rate of 0.0125 and 0.0128 for several users and attempts.