CLASSIFICATION

Classification was carried out by calculating the Euclidean distance between the stored templates and feature vectors extracted from the test signal. So as to combine the matching distances or scores of the three feature vectors, score normalisation was carried out. The normalisation of matching distances was done using Min-Max normalisation, such that given a set of matching distances {dk}, к = 1, 2, Ъ...п, the normalised distances were obtained by utilising the expression mentioned below:

Comparison of feature vectors for two subjects

FIGURE 13.5 Comparison of feature vectors for two subjects.

where sk is the matching score after normalisation, and min and max are the minimum and maximum values of [dk], respectively. Score-level fusion by employing simple weighted fusion was adopted for the final identification or verification decision. The combined score was computed as follows:

where sh s2, and s3 are the normalised scores of matching distances for the corresponding three feature vectors; vvt, vv2, and vv3 are their weights; and S is the combined score. Experiments were conducted to compute identification rate as well as the verification rate.

EXPERIMENTS AND RESULTS

The approach was tested on a dataset consisting of samples from eleven subjects recorded in two sessions. Evaluations were carried out for both within-session and across-session settings. The template for each subject was created from the first part of the recording of first session, and the same template was also used for across- session testing. The feature vectors for the within-session testing were extracted from the last 10 seconds of the input signal. The across-session testing was carried out by extracting the features vectors for the signal samples taken from 10-to 20-second segment of the second set of recordings.

The results achieved for the identification and verification tasks have been listed in Table 13.3. For the within-session experiments, the rank 1 identification rate of 81.82% (9 out of 11) was achieved for the method described in this section. On the other hand, the verification rate for this setting was 90.91%. When the testing was done for across-session setting, the rank 1 identification rate dropped to 54.55% (6 out of 11). The verification performance for across session also dipped to 63.64%. Further analysis of the results for identification accuracy can be done by viewing the

TABLE 13.3

Accuracy for Within- and Across-Session Testing

Mode

Session

Within Session (%)

Across Session (%)

Identification

81.82

54.55

Verification

90.91

63.64

CMC plot for within and across session testing

FIGURE 13.6 CMC plot for within and across session testing.

Cumulative Match Curve (CMC) plot shown in Figure 13.6. It can be seen that for within session, 100% subjects are recognised by rank 3, and for the across-session scenario, 8 out of the 11 subjects are identified by rank 5. This shows that PPG signal contains content that is characteristic of a particular human being. In this case, the recognition tasks have been performed using simple Euclidean distance classifier. As in the reported literature, machine learning and deep learning techniques have been found to have better classification abilities, so it is expected that using them as classifiers should provide higher accuracy.

CONCLUSIONS

A review of evolution of PPG for biometric application, since its inception in this area, has been presented in this chapter. An approach using multiple feature vectors for PPG biometric has also been explained. Based upon the survey of literature and results obtained for the method described here, it can be stated that PPG signal has a potential to be utilised for the biometric applications. Moreover, the accuracy for the across-session testing is expected to improve by using robust classifiers offered by the new age machine and deep learning techniques. However, the studies published in this area have been carried out on small datasets with less than fifty subjects. In order to establish the efficacy of the methods, experiments need to be carried out on datasets having multiple session recordings and population size of few hundreds. The future work in this direction will be undertaken by keeping these points in mind.

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