A novel portable iris recognition system and usability evaluation

Jang, Youngkyoon; Kang, Byung Jun; Park, Kang Ryoung

doi:10.1007/s12555-010-0112-0

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…In addition, similar to the research on other biosignals, research is being conducted on personal identification for the authorization of access to smart systems. Because EMG signals can be measured only when a person contracts his/her muscles, they are more reliable for user identification than passively measurable variables, such as the face [1] or fingerprints [2,3], in authentication problems. Faces or fingerprints can be captured by others using noncontact capture devices, whereas EMG signals can only be generated while individuals are alive and are guided by their intentions.…”

Section: Introductionmentioning

confidence: 99%

Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals

Byeon,

Kwak

2023

Electronics

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This study focuses on personal identification using bidirectional long short-term memory (LSTM) with efficient features from electromyogram (EMG) biomedical signals. Personal identification is performed by comparing and analyzing features that can be stably identified and are not significantly affected by noise. For this purpose, 13 efficient features, such as enhanced wavelength, zero crossing, and mean absolute value, were obtained from EMG signals. These features were extracted from segmented signals of a specific length. Then, the bidirectional LSTM was trained on the selected features as sequential data. The features were ranked based on their classification performance. Finally, the most effective features were selected, and the selected features were connected to achieve an improved classification rate. Two public EMG datasets were used to evaluate the proposed model. The first database was acquired from eight-channel Myo bands and was composed of EMG signals from 10 varying motions of 50 individuals. The total numbers of segments for the training and test sets were 30,000 and 20,000, respectively. The second dataset consisted of ten arm motions acquired from 40 individuals. A performance comparison of the dataset revealed that the proposed method exhibited good performance and efficiency compared to other well-known methods.

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Section: Introductionmentioning

confidence: 99%

Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals

Byeon,

Kwak

2023

Electronics

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“…The characteristics used in biometric study include face [ 7 ], iris [ 8 ], gait [ 9 ], sweat pore [ 10 ], finger-vein [ 11 ], palmprint, palmvein [ 12 ], voice [ 13 ], handwritten signature [ 14 ], and electrocardiogram (ECG) [ 15 ]. The disadvantages of each characteristic include the face having restrictions on glasses, mask, and makeup, and the original face for recognition should be recovered.…”

Section: Introductionmentioning

confidence: 99%

“…The handwritten signature needs a pen to enter data, the hand is busy during writing, and the hand-written signature can be easily intentionally copied. The electrocardiogram is sensitive to the state and noise of the measuring signal [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

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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“…Several characteristics of the body have been studied in the field of biometrics such as the face [8], fingerprints [9], iris [10], and others [11][12][13][14][15][16]. Face recognition normally uses color images captured from an RGB sensor.…”

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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A novel portable iris recognition system and usability evaluation

Cited by 9 publications

References 16 publications

Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals

Personal Identification Using Long Short-Term Memory with Efficient Features of Electromyogram Biomedical Signals

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

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