ECG-based biometric human identification based on backpropagation neural network

Biometric human identifications are expansively reshaping security applications in the emerging sophisticated era of smart devices. To inflate the level of security and privacy demands, human physiological signal based human identification and authentication systems are getting tremendous attention. This study focuses on producing feasible amount of segmented signals from a source signal for training dataset, and integrating 2-layer framework backpropagation neural network to handle the great amount of classes for identification without hesitation. The results suggest that the proposed method surpasses the recent technique with the similar architecture, and possesses more advantages in terms of computational complexity and high performance compared with the previously reported study.