Key result
A nonfiducial PPG-based subject authentication system using discrete wavelet transform and a support vector machine classifier achieved an average authentication accuracy of 99.3%.
Why the study?
Cost and reliability are significant limitations of existing user authentication technologies, such as fingerprints and iris recognition, as lifestyle shifts toward smart devices and e-services.
A nonfiducial PPG-based authentication system using DWT and SVM achieved 99.3% accuracy, offering a potential low-cost biometric verification method.
Nowadays, there is a global change in lifestyle that is moving more toward the use of e-services and smart devices which necessitate the verification of user identity. Different organizations have put into place a range of technologies, hardware, and/or software to authenticate users using fingerprints, iris recognition, and so forth. However, cost and reliability are significant limitations to the use of such technologies. This study presents a nonfiducial PPG-based subject authentication system. In particular, the photoplethysmogram (PPG) signal is first filtered into four signals using the discrete wavelet transform (DWT) and then segmented into frames. Ten simple statistical features are extracted from the frame of each signal band to compose the feature vector. Augmenting the feature vector with the same features extracted from the 1st derivative of the corresponding signal is investigated, along with different fusion approaches. A support vector machine (SVM) classifier is then employed for the purpose of identity authentication. The proposed authentication system achieved an average authentication accuracy of 99.3% using a 15 sec frame length with the augmented multiband approach.
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Alotaiby et al. (2020) studied Subject authentication. Nonfiducial PPG-based subject authentication system was evaluated on Average authentication accuracy. A nonfiducial PPG-based subject authentication system using discrete wavelet transform and a support vector machine classifier achieved an average authentication accuracy of 99.3%.
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