Cancelable biometric authentication system based on ECG

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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“…• Each feature vector was normalized using the Gram-Schmidt ortho-normalization to produce the normalization feature matrix [30].…”

Section: E Updating Of Ecg and Fingerprint Templates (Cancelable Metmentioning

confidence: 99%

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

2019

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“…Step 5: TD The feature tensor is decomposed by Tucker, and then the low-dimensional core tensor G and three orthogonal matrices U (1,2,3) are recombined to obtain the target tensor W .…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…At present, speech authentication methods mainly include watermarking technology and digital signature. The disadvantage of watermarking technology is that the original data will be modified and the quality of the speech will be degraded after embedding the watermark [1], [2], [3], [36]. Digital signature technology is too sensitive to changes in the binary level of speech data to be suitable for speech content [35].…”

Section: Introductionmentioning

confidence: 99%

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A Long Sequence Speech Perceptual Hashing Authentication Algorithm Based on Constant Q Transform and Tensor Decomposition

et al. 2020

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Most speech authentication algorithms are over-optimized for robustness and efficiency, resulting in poor discrimination. Hashing shorter sequence is likely to cause the same hashing sequence to come from different speech segments, which will cause serious deviations in authentication. Few people pay attention to the research on the discrimination of hashing sequence length, so this paper proposes a long sequence speech authentication algorithm based on constant Q transform (CQT) and tensor decomposition (TD). In this paper, hashing long sequence is used to solve the problem of poor collision resistance of existing algorithms, fast and accurate authentication can be achieved for important speech fragments with large data volumes. The sub-band in the frequency domain are first divided into different matrix, then the variance set of sub-band in the frequency domain is obtained, and finally the feature values are obtained by CQT and TD transformation. The obtained feature values have strong robustness and can cope with the interference of complex channel environment. In this paper, Texas Instruments and Massachusetts Institute of Technology (TIMIT) speech database and the Text to Speech (TTS) are used to establish a database of 51600 speeches to verify the performance of the algorithm. Experimental results show that compared with the existing speech authentication algorithms, the proposed algorithm has the characteristics of high discrimination, strong robustness and high efficiency.

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“…In contrast, electrocardiogram (ECG) signals, which exhibit unique physiological characteristics across subjects related to the placement and size of the heart, are difficult to spoof because the underlying biometric features are concealed during authentication and can only be obtained from physical measurements on the subject [ 12 , 13 , 14 ]. Since Biel et al [ 15 ] first studied using ECG signal processing for the biometric recognition, ECG-based biometric authentication has received great attention as a next-generation promising technique and been implemented with various approaches to improve the authentication performance for the past few decades [ 16 , 17 , 18 ]. However, ECG signals of a person may vary according to his/her physical state or health condition, possibly leading to authentication failure in some cases [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

Kim

Yang

Lee

et al. 2021

Sensors

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Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

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Cancelable biometric authentication system based on ECG

Cited by 57 publications

References 34 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

A Long Sequence Speech Perceptual Hashing Authentication Algorithm Based on Constant Q Transform and Tensor Decomposition

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning

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