In this paper, we propose a deep Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) in a bidirectional manner (BGRU) for human identification from electrocardiogram (ECG) based biometrics, a classification task which aims to identify a subject from a given time-series sequential data. Despite having a major issue in traditional RNN networks which they learn representations from previous time sequences, bidirectional is designed to learn the representations from future time steps which enables for better understanding of context, and eliminate ambiguity. Moreover, GRU cell in RNNs deploys an update gate and a reset gate in a hidden state layer which is computationally efficient than a usual LSTM network due to the reduction of gates. The experimental results suggest that our proposed BGRU model, the combination of RNN with GRU cell unit in bidirectional manner, achieved a high classification accuracy of 98.55%. Various neural network architectures with different parameters are also evaluated for different approaches, including one-dimensional Convolutional Neural Network (1D-CNN), and traditional RNNs with LSTM and GRU for non-fiducial approach. The proposed models were evaluated with two publicly available datasets: ECG-ID Database (ECGID) and MIT-BIH Arrhythmia Database (MITDB). This paper is expected to demonstrate the feasibility and effectiveness of applying various deep learning approaches to biometric identification and also evaluate the effect of network performance on classification accuracy according to the changes in percentage of training dataset. INDEX TERMS 1D-convolutional neural networks, bidriectional recurrent neural networks, biometrics classification, ECG signals, gated recurrent unit, user identification, signal processing.

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG.

Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

The risk of tampering exists for conventional user recognition methods based on biometrics such as face and fingerprint. Recently, research on user recognition using biometric signals such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) has been actively performed to overcome this issue. We herein propose a user recognition method applying a deep learning technique based on ensemble networks after transforming ECG signals into two-dimensional (2D) images. A preprocessing process for one-dimensional ECG signals is performed to remove noise or distortion; subsequently, they are projected onto a 2D image space and transformed into image data. For the proposed algorithm, we designed deep learning-based ensemble networks to improve the degraded performance arising from overfitting in a single network. Our experimental results demonstrate that the proposed ensemble networks exhibit an accuracy that is 1.7% higher than that of the single network. In particular, the performance of the ensemble networks is up to 13% higher compared to the single network that degrades the recognition rate by displaying similar features between classes.

Biometric-based authentication can provide a strong security guarantee of the identity of users. However, the security of biometric data is particularly important as any compromise of the biometric data will be permanent. In this paper, we propose a secure and efficient protocol to transmit fingerprint images from a fingerprint sensor to a client by exploiting the characteristics of the fingerprint images. Because the fingerprint sensor is computationally limited, a standard encryption algorithm may not be applied to the full fingerprint images in real-time to guarantee the integrity and confidentiality of the fingerprint images transmitted. To reduce the computational workload on the resource-constrained sensor, we apply the encryption algorithm to a nonce for integrity and to a specific bitplane of each pixel of the fingerprint image for confidentiality. Experimental results show that the integrity and confidentiality of the fingerprint images can be guaranteed without any leakage of the fingerprint ridge information and can be completed in real-time on embedded processors.