Research Article A Nonfiducial PPG-Based Subject Authentication Approach Using the Statistical Features of DWT-Based Filtered Signals Turky N. Alotaiby , 1 Fatima Aljabarti, 2 Gaseb Alotibi, 3 and Saleh A. Alshebeili 4 1 KACST, Saudi Arabia 2 Prince Sultan University, Saudi Arabia 3 Public Security, Saudi Arabia 4 Department of Elect. Engineering, King Saud University, Saudi Arabia Correspondence should be addressed to Turky N. Alotaiby; totaiby@kacst.edu.sa Received 2 March 2020; Revised 25 August 2020; Accepted 21 September 2020; Published 17 October 2020 Academic Editor: Davide Palumbo Copyright © 2020 Turky N. Alotaiby et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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 1 st 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. 1. Introduction Today, with an increasing dependency on the information systems, identity authentication has become an essential part of life. From accessing mobile phones to performing online financial transactions, a user needs to authenticate his/her identity. Generally, there are three approaches for user authentication: (1) soft keys (e.g., passwords), (2) hard keys (e.g., smart cards), and (3) biometrics [1]. Traditional authen- tication techniques such as account passwords and smart cards are still widely used, but they are not reliable enough because they are easily stolen, lost, forgotten, and forged. In the recent years, biometric-based identity authentication has been gaining more attention. The International Organization for Standardization (ISO) defined biometrics as the auto- mated recognition of individuals based on their behavioral and biological characteristics [2]. Biometric-based identity authentication is aimed at uniquely identifying individuals based on their physiological and/or behavioral characteristics such as fingerprint, face, retina, palm print, lip movement, gait, DNA, voice, EEG, or ECG [2–19]. In 2003, Gu et al. presented the first attempt at using a PPG signal for identity authentication [20, 21]. Hertzman and Spiel- man in 1973 described the PPG [22] and a typical PPG device, which consists of two parts: the light source and the photode- tector. The light source emits light to be reflected off tissue while the photodetector detects and measures the reflected light, which is proportional to blood volume variation [23]. Different light colors are used for different applications, where the most popular colors are red and green [24]. The PPG-based identity authentication technique has diverse advantages over other biometric approaches because it is easy to set up, is sim- ple to implement, has low cost, and could be placed comfort- ably in different parts of the body such as the finger or wrist. Hindawi Journal of Sensors Volume 2020, Article ID 8849845, 14 pages https://doi.org/10.1155/2020/8849845