Table 1.
Speed Rate (SR) range values for some state-of-the-art biometric techniques.
Table 2.
List of examples of biometric algorithms tested after all subjects to be identified were enrolled in the system, across the last 15 years.
Fig 1.
Schematic representation of FRR versus FAR curve for several state-of-the-art biometric techniques.
Face and Face(2)—two face-recognition systems validated using different devices; FP-chip and FP-chip(2)—fingerprint recognition through a chip sensor tested with two different methods/devices; FP-optical—optical-based fingerprint recognition; Hand—hand-based biometrics; Iris—iris-based recognition; Vein—vein patterns-based recognition; Voice—voice recognition. The devices/systems used to validate each one of those biometric techniques are referred to and described in the study of Mansfield et al. [17]. Graphic generated based on the results of Mansfield et al. [17].
Fig 2.
Three examples of ECG acquisitions from three subjects (different from the ones whose ECG information was used in the classification) acquired using the one-lead VitalJacket™ wearable platform [72, 73], with fiducial points P, Q, R, S, and T that define the heartbeat morphology.
(a) Male subject; 59 years; RR = 736 ms. (b) Male subject; 25 years; RR = 660 ms. (c) Female subject; 28 years; RR = 656 ms.
Table 3.
Review of biometric-related technologies used in entertainment applications and for other purposes—Part I.
Table 4.
Review of biometric-related technologies used in entertainment applications and other purposes—Part II.
Fig 3.
Scheme illustrating the steps of the algorithm.
First, the raw signal was filtered (1) and, then, the fiducial points were located (2). After that, the distance measures were computed (3) and noisy heartbeats were removed (4). In the training phase, the distance measures were therefore normalized according to subjects’ heart rate (5). The training features were used to optimize the SVM classifier settings and to build the most suitable training model (6)—part of the figure adapted from [102]. In the test phase, after obtaining the processed data, the test vectors were mapped into the training feature space using the model built during the training phase and the mean RR value across subjects to obtain the predicted label (7).
Table 5.
Dataset description in terms of gender, mean age of individuals, and length of ECG acquisitions.
Fig 4.
Portion of ECG signal uploaded from the PTB Diagnostic ECG Database processed using the algorithm proposed here.
Portion of ECG signal uploaded from the PTB Diagnostic ECG Database processed using the algorithm proposed here—participant number 238 (acquisition reference: s0466). Identification of fiducial points Q, R, S, and T.
Table 6.
Average number of heartbeats across all 66 combinations.
Fig 5.
Scheme illustrating the feature normalization procedure, based on the average temporal distance between consecutive R points across subjects.
N represents the number of combinations (from the 66 between the training and test sets) considered.
Fig 6.
Scheme explaining the classification of heartbeats of each one of the 66 test runs generated for each training duration (10, 20, 30, 40, 50, 60, 70, 80, 90, 100 seconds).
t represents the data blocks belonging to each one of the 66 test sets; r represents each block of the 10 seconds-training set of the 66 sets generated for the duration of each training time duration; i represents the number of different combinations evaluated. The procedure illustrated by this scheme was repeated for each training time (10, 20, 30, 40, 50, 60, 70, 80, 90, 100 seconds).
Fig 7.
Mean test accuracy obtained for the 66 training and test runs generated, for each training duration, and corresponding standard error bars.
Fig 8.
Beat-to-beat analysis performance results.
a) Number of heartbeats averaged across the 10 subjects and 500 runs necessary to identify each subject individually and the corresponding fitted line; (b) Evolution of maximum and minimum number of heartbeats with the training duration.
Fig 9.
Schematic representation of FRR versus FAR curves for several state-of-the-art techniques in comparison with our method (represented by the yellow triangle).
Face and Face(2)—two systems of a face recognition technique validated using different devices; FP-chip and FP-chip(2)—fingerprint recognition through chip sensor tested with two different methods/devices; FP-optical—optical-based fingerprint recognition; Hand—hand-based biometrics; Iris—iris-based recognition; Vein—vein pattern-based recognition; Voice—voice recognition. The devices/systems used to validate each one of these biometric techniques are mentioned and described in the study of Mansfield et al. [17]. Graphic generated considering the results obtained in [17, 141].
Table 7.
Training estimated optimal parameters for the present method achieving an averaged authentication Speed Rate (SR) of 1.02 heartbeats.