This paper presents a new feature extraction technique called Pulse Active Mean (PAM) implemented on Electrocardiograms (ECG) for biometric authentication. A doubly secure ECG authentication framework is proposed which makes use of the important attributes of the PAM algorithm as a personal identification number (PIN). The PIN is used to extract different locations of ECG characteristics generating unique feature vectors. The presence of the correct PIN and ECG signals make the proposed authentication framework doubly secure. The performance of PAM is evaluated by comparing its receiver operating characteristic (ROC) curve with traditional temporal and amplitude feature extraction techniques on 100 Physikalisch-Technische Bundesanstalt (PTB) subjects. The evaluation of the biometric performance when different values of PIN are presented is also investigated. It is shown in this paper that different PIN values generate different feature vector sets while still providing consistent authentication performance.