The electrocardiogram (ECG) is becoming a promising technology for biometric human identification. Usually ECG is used for health measurements and this is useful for biometric application to state that the subject under analysis is alive.
But an individual identification should not require a classical ECG clinical analysis where several contacts are applied to the person to be identified.
In literature, ECG biometric recognition is usually studied for the recognition of a subject within a group pf known subjects.
The aim of the designed embedded wearable controller is to authorize a subject or to reject him, labeling as an intruder unknown to the system.
The research uses 40 healthy subjects: 2 authorized and 38 intruders.
A one-lead ECG trace has been recorder from the wrists of subjects, features have been extracted using a combination of Autocorrelation and Discrete Cosine Transform (AC/DCT) and then classified using a Multilayer Perceptron.
Results show that intruder recognition can be performed with a rate of success equal to 100%.