Open AccessECG based person authentication using empirical mode decomposition and discriminant analysis
Author(s) -
Sugondo Hadiyoso,
Achmad Rizal,
Suci Aulia
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1367/1/012014
Subject(s) - biometrics , linear discriminant analysis , computer science , pattern recognition (psychology) , artificial intelligence , authentication (law) , discriminant , feature extraction , modality (human–computer interaction) , subspace topology , hilbert–huang transform , identification (biology) , feature (linguistics) , mode (computer interface) , speech recognition , data mining , computer vision , computer security , human–computer interaction , linguistics , philosophy , botany , filter (signal processing) , biology
Person identification or authentication through biometric features has been widely applied for basic access and high-level security. But conventional biometrics such as fingerprints and irises tend to be easily faked or duplicated. Therefore a new biometric modality is needed to overcome that problem. In this paper, we simulate a new model of biometric systems using physical signals of the body. The proposed biometric system is based on ECG signals as a characteristic of each subject. A total of 110 raw ECG signals with a duration of 5 seconds from 11 participants were demonstrated in the proposed system. Empirical mode decomposition (EMD) and statistical analysis are used for feature extraction. Discriminant analysis with cross-validation was applied to test the performance of the proposed method. In this research, the highest accuracy of 93.6% was obtained using subspace discriminant in the scenario of all feature attributes as predictors.