Priority-based Multi-feature Vector Model Using Convolution Neural Network for Biometric Authentication

Abstract

Many societal institutions have increased standards for the efficacy and dependability of identification systems in response to the ever-increasing sophistication of computer technology. Biometric identity systems have mostly supplanted the usage of conventional key and smart card systems in highly secure industries. There has been a dramatic increase in the adoption of biometric authentication systems in the last decade. While many biometric systems have relied on exterior physiological traits like fingerprints, iris scans, palm prints, and faces, very few have investigated the possibility of using interior physiological traits as a biometric. It is challenging for the benefits of unimodal biometric technology to be realized in real-world applications because to its inherent constraints, such as the fact that it only proposes a single piece of information and that environmental factors can impact data verification. While passwords have served their purpose of authenticating users and controlling access, they have also revealed their weaknesses. Concerns about forging or impersonating authentication have prompted the proposal of various Artificial Intelligence (AI)-based solutions. A lot of people are interested in user authentication based on ECGs these days. Unlike other biometrics, an electrocardiogram verifies that the person is actual and alive, making it one of the most trustworthy advanced authentication methods. The P, Q, R, S, and T characteristic points of an ECG signal are responsible for its most salient features. Feature selection is a statistical method that can be implemented in a supervised or unsupervised setting via regression or classification. A Convolution Neural Network (CNN) is a type of neural network that extracts and feeds into another neural network, which then classifies those extracted features. The ECG signal serves as input to a feature extraction network. The neural network performs categorization based on the retrieved feature signals. To overcome the problem that the recognition accuracy of traditional ECG identification methods declines with the rise in the number of testing samples at various moments or throughout different heartbeat cycles, this research offers a multi feature reuse model. In this research, an Associated Priority-based Weighted Multi-Feature Vector model using Convolution Neural Network (APbWMFV-CNN) is proposed for ECG signal-based authentication. The proposed model selects the most appropriate features used for accurate biometric model. The comparison results represent that the proposed model performance in user detection is high.