An enhanced intrinsic biometric in identifying people by photopleythsmography signal

Abstract

In the area of secure authentication, the fusion of Photopleythsmography (PPG) signals for biometric identification is a novel technique. Researchers suggested the use of PPG along with other biometric components for augmenting the biometric robustness. PPG signals have great potential to serve as biometric identification appliance and can be easily obtained with low cost. Use of PPG signals for personnel identification is very appropriate during field operations in day or night. While building a large scale identification system the feature selection from PPG is a critical activity. To have the identification system more accurate, the set of features that deemed to be the most effective attributes are extracted in order to build robust identification system. Applying Kernel Principal Component analysis (KPCA) an efficient supervised learning method for dimensionality reduction and feature extraction in this experiment results in precise classification. © 2013 Springer.