A novel biometric based on ECG signals and images for human authentication

This paper represents a complete system for using Electrocardiogram (ECG) images for human authentication. In this study, the proposed algorithm is divided into three main stages: Pre-processing stage, feature extraction stage and classification stage. A real database is used; it consists of 120 ECG images which are collected from 20 persons. The preprocessing stage is done on the ECG image. Preprocessing should remove all variations and details from an ECG image that are meaningless to the authentication method. In addition, this paper discusses briefly an extended version of work previously published on ECG feature extraction. In classification stage, neural network is used to make persons authentication. At the end, a system for real-time authentication is built. The proposed system achieves high sensitivity results for extracting ECG features and for human authentication.