Cognitive-Biometric Recognition From Language Usage: A Feasibility Study

Pokhriyal, Neeti; Tayal, Kshitij; Nwogu, Ifeoma; Govindaraju, Venu

doi:10.1109/tifs.2016.2604213

Cited by 21 publications

(14 citation statements)

References 21 publications

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“…If a service provider responds to requests indiscriminately, it may be exploited for crime, so it should be limited to the authorized person, and the required technology is authentication. Authentication methods of security-dependent services, such as access to private clouds and payments using smartphone banking, are moving toward applying convenient biometrics [ 1 , 2 , 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Byeon

Pan

Kwak

2019

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Byeon

Pan

Kwak

2019

Sensors

View full text Add to dashboard Cite

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“…Considering the security requirements of any company/organization, biometrics automatically permit access only to those whose identities have been verified. It also allows the automatic authorization of cash settlements or remittances even on online platforms [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Pre-Configured Deep Convolutional Neural Networks with Various Time-Frequency Representations for Biometrics from ECG Signals

Byeon

Kwak

2019

Applied Sciences

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We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations. Biometrics technology records a person’s physical or behavioral characteristics in a digital signal via a sensor and analyzes it to identify the person. An ECG signal is obtained by detecting and amplifying a minute electrical signal flowing on the skin using a noninvasive electrode when the heart muscle depolarizes at each heartbeat. In biometrics, the ECG is especially advantageous in security applications because the heart is located within the body and moves while the subject is alive. However, a few body states generate noisy biometrics. The analysis of signals in the frequency domain has a robust effect on the noise. As the ECG is noise-sensitive, various studies have applied time-frequency transformations that are robust to noise, with CNNs achieving a good performance in image classification. Studies have applied time-frequency representations of the 1D ECG signals to 2D CNNs using transforms like MFCC (mel frequency cepstrum coefficient), spectrogram, log spectrogram, mel spectrogram, and scalogram. CNNs have various pre-configured models such as VGGNet, GoogLeNet, ResNet, and DenseNet. Combinations of the time-frequency representations and pre-configured CNN models have not been investigated. In this study, we employed the PTB (Physikalisch-Technische Bundesanstalt)-ECG and CU (Chosun University)-ECG databases. The MFCC accuracies were 0.45%, 2.60%, 3.90%, and 0.25% higher than the spectrogram, log spectrogram, mel spectrogram, and scalogram accuracies, respectively. The Xception accuracies were 3.91%, 0.84%, and 1.14% higher than the VGGNet-19, ResNet-101, and DenseNet-201 accuracies, respectively.

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“…Individual identification is a technique that is used to identify a user using behavioral or physical characteristics that are the sole characteristics of an individual. Currently, the range of services, such as security, banking, access control, medical care, and entertainment, is expanding, and so individual identification methods are studied using diverse characteristics [1][2][3][4][5][6]. Such individual identification using the face, fingerprint, and so forth has vulnerability to falsification and disguise, and it must be moved to a special place where a system is installed for individual identification [7].…”

Section: Introductionmentioning

confidence: 99%

Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

et al. 2018

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In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown in the original structure. In contrast with unsupervised linear subspace learning (LSL) techniques such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), it is less susceptible to small-data problems because it learns more compact and potentially useful representations, and it can efficiently handle large tensors. Here, the third-order tensor is formed by reordering the one-dimensional ECG signal into a two-dimensional matrix, considering the time frame. The MSL consists of four steps. The first step is preprocessing, in which input samples are centered. The second step is initialization, in which eigen decomposition is performed and the most significant eigenvectors are selected. The third step is local optimization, in which input data is applied by eigenvectors from the second step, and new eigenvectors are calculated using the applied input data. The final step is projection, in which the resultant feature tensors after projection are obtained. The experiments are performed on two databases for performance evaluation. The Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, and Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The experimental results revealed that the tensor-based MEECG and MFECG showed good identification performance in comparison to PCA and LDA of LSL.

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Cognitive-Biometric Recognition From Language Usage: A Feasibility Study

Cited by 21 publications

References 21 publications

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Pre-Configured Deep Convolutional Neural Networks with Various Time-Frequency Representations for Biometrics from ECG Signals

Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

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