Abstract
Human identification is considered as a serious challenge for several applications such as cybersecurity and access control. Recently, the trend of human identification has been directed to human biometrics, which can be used to recognize persons based on some physiological or behavioral characteristics that they own, such as fingerprint, iris, and biosignals. There are several types of human biosignals including electroencephalography (EEG), electrocardiography (ECG), and photoplethysmography (PPG) signals. This paper presents a human identification system based on PPG signals. The proposed system consists of three main phases: signal acquisition, signal pre‐processing, and feature extraction/classification. The pre‐processing phase involves denoising of the acquired signal, transformation of the 1D signal sequence into a 2D image, and computation of the spectrogram. Feature extraction is carried out on the images obtained from the pre‐processing phase. Features are extracted from the images based on convolutional neural networks (CNNs). The proposed CNN model consists of a sequence of convolutional (CNV) and pooling layers. Finally, the obtained feature maps are fed to the classifier to discriminate human identities. The proposed identification algorithm is applied on signals with and without an additive white Gaussian noise (AWGN). The simulation results reveal that the proposed algorithm achieves an accuracy of 99.5% with the spectrogram representation and 89.8% with the 2D image representation, in the absence of noise. In addition, the paper gives a discussion of the efficiency of denoising techniques such as wavelet denoising, Savitzky–Golay and Kalman filtering, when involved with the proposed algorithm. The simulation results prove that the wavelet dencoising technique has a best performance among the discussed noise reduction techniques.
This paper presents a human identification system based on PPG signals and CNNs. The proposed system starts by acquiring the PPG signals from the fingertips of the enrolled subjects using biosensors. A pre‐processing step is involved for noise reduction. Then, the 1D PPG signal is converted to an image with either spectrogram estimation or 1D to 2D image rearrangement. Finally, the CNN classifier is used to extract and classify features from the obtained images.