A novel biometric authentication approach using electrocardiogram signals

Gürkan, Hakan; Güz, Ümit; Yarman, Binboğa Sıddık

doi:10.1109/embc.2013.6610486

Cited by 17 publications

(16 citation statements)

References 13 publications

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“…To evaluate the performance of the proposed ECG system on the two databases, ten-fold cross-validation approach was used, which divided the total ECG images into ten equal parts, nine out of ten parts were used for training and the remaining were used for testing. In terms of authentication accuracies, we used internal fusions by addition and by concatenation respectively, and then compared our algorithm with previous authentication algorithms [36]- [39]. To the best of our knowledge, there are only two recent works on ECG authentication based on deep learning [40], [41], thus our study should be considered one of the first studies to build an ECG authentication system based on deep learning.…”

Section: Ecg Authenticationmentioning

confidence: 99%

Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint

2019

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A multimodal biometric system integrates information from more than one biometric modality to improve the performance of each individual biometric system and make the system robust to spoof attacks. In this paper, we propose a secure multimodal biometric system that uses convolution neural network (CNN) and Q-Gaussian multi support vector machine (QG-MSVM) based on a different level fusion. We developed two authentication systems with two different level fusion algorithms: a feature level fusion and a decision level fusion. The feature extraction for individual modalities is performed using CNN. In this step, we selected two layers from CNN that achieved the highest accuracy, in which each layer is regarded as separated feature descriptors. After that, we combined them using the proposed internal fusion to generate the biometric templates. In the next step, we applied one of the cancelable biometric techniques to protect these templates and increase the security of the proposed system. In the authentication stage, we applied QG-MSVM as a classifier for authentication to improve the performance. Our systems were tested on several publicly available databases for ECG and fingerprint. The experimental results show that the proposed multimodal systems are efficient, robust, and reliable than existing multimodal authentication systems.

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Section: Ecg Authenticationmentioning

confidence: 99%

Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint

2019

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“…Experiments demonstrated that the features extracted from one lead can be used to identify a person with 98% accuracy. To date, ECG has seen significant attention in biometric recognition studies [8,25,34,54], using both fiducial (specific anchor points such as P-QRST) and non-fiducial features. Despite the research efforts directed at ECG biometrics, at present there is only one commercial system available which exploits ECG or EMG biometrics for recognition, the B-secure [28].…”

Section: Related Workmentioning

confidence: 99%

“…Until now, one of the most popular strategies is fusion modalities. Thus, methods based on score fusion have achieved remarkable performance, most of which fuses two modalities [11,25,34]. As for ECG with the conventional biometric, there are fewer studies.…”

Section: Related Workmentioning

confidence: 99%

“…A remote login process by ECG signal captured from a finger, which improves security and privacy [31], unlike such traditional modalities as the face that can be found easily in public. As reported in reviews of the state-of-the-art ECG based biometric systems, such a biometric system must record more than 10 s of ECG data (long term) in order to make a reliable decision about human identity [8,11,25].…”

Section: Introductionmentioning

confidence: 99%

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Multimodal biometric system for ECG, ear and iris recognition based on local descriptors

Regouid

Touahria

Benouis

et al. 2019

Multimed Tools Appl

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Combination of multiple information extracted from different biometric modalities in multimodal biometric recognition system aims to solve the different drawbacks encountered in a unimodal biometric system. Fusion of many biometrics has proposed such as face, fingerprint, iris…etc. Recently, electrocardiograms (ECG) have been used as a new biometric technology in unimodal and multimodal biometric recognition system. ECG provides inherent the characteristic of liveness of a person, making it hard to spoof compared to other biometric techniques. Ear biometrics present a rich and stable source of information over an acceptable period of human life. Iris biometrics have been embedded with different biometric modalities such as fingerprint, face and palm print, because of their higher accuracy and reliability. In this paper, a new multimodal biometric system based ECG-eariris biometrics at feature level is proposed. Preprocessing techniques including normalization and segmentation are applied to ECG, ear and iris biometrics. Then, Local texture descriptors, namely 1D-LBP (One D-Local Binary Patterns), Shifted-1D-LBP and 1D-MR-LBP (Multi-Resolution) are used to extract the important features from the ECG signal and convert the ear and iris images to a 1D signals. KNN and RBF are used for matching to classify an unknown user into the genuine or impostor. The developed system is validated using the benchmark ID-ECG and USTB1, USTB2 and AMI ear and CASIA v1 iris databases. The experimental results demonstrate that the proposed approach outperforms unimodal biometric system. A Correct Recognition Rate (CRR) of 100% is achieved with an Equal Error Rate (EER) of 0.5%.

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“…Gurkan et al . [ 18 ] were used a combination of 20 AC/DCT features and 13 Mel-frequency cepstral coefficient features. In addition, information of the QRS complex have been considered as the third feature set using 120 samples of ECG signal around the R-peak (60 samples each side of R-peak).…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram based identification using a new effective intelligent selection of fused features

2015

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Over the years, the feasibility of using Electrocardiogram (ECG) signal for human identification issue has been investigated, and some methods have been suggested. In this research, a new effective intelligent feature selection method from ECG signals has been proposed. This method is developed in such a way that it is able to select important features that are necessary for identification using analysis of the ECG signals. For this purpose, after ECG signal preprocessing, its characterizing features were extracted and then compressed using the cosine transform. The more effective features in the identification, among the characterizing features, are selected using a combination of the genetic algorithm and artificial neural networks. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, MITBIH Normal Sinus Rhythm Database and The European ST-T Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Identification rates of 99.89% and 99.84% and 99.99% are obtained for these databases respectively. The proposed algorithm exhibits remarkable identification accuracies not only with normal ECG signals, but also in the presence of various arrhythmias. Simulation results showed that the proposed method despite the low number of selected features has a high performance in identification task.

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A novel biometric authentication approach using electrocardiogram signals

Cited by 17 publications

References 13 publications

Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint

Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint

Multimodal biometric system for ECG, ear and iris recognition based on local descriptors

Electrocardiogram based identification using a new effective intelligent selection of fused features

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