Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

Labati, Ruggero Donida; Piuri, Vincenzo; Sassi, Roberto; Scotti, Fabio; Sforza, Gianluca

doi:10.1109/cibim.2014.7015440

Cited by 14 publications

(21 citation statements)

References 36 publications

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“…It is relevant here to mention that varying levels of physical, mental, or emotional stimulations are known to affect heart rate. Unfortunately, recordings under these 6 Journal of Sensors stimulations are not available in PTB dataset. Therefore, the robustness of the proposed identification method under the effect of these stimulations will be a research point to consider in our future work.…”

Section: Resultsmentioning

confidence: 99%

“…Biometric recognition aims at uniquely identifying the individuals based on their physiological and/or behavior characteristics such as fingerprint, face, retina, palm print, gait, or speech [1]. Today, subject identification is vital for many applications including financial transactions, e-commerce, data protection, access control, entertainment, voting, and health [2][3][4][5][6][7][8][9]. However, the various biometrics that are currently being adopted exhibit different issues related to performance, measurability, robustness, and liveness detection [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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ECG-Based Subject Identification Using Common Spatial Pattern and SVM

et al. 2019

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In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

et al. 2019

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“…In order to perform classification, half of each of the subjects of the NSR, PTB, and QT DB are set as the training data, and the remaining data is set as test. Euclidean distance, as a method of obtaining the distance between two points, is defined as Equation (7) and SVM, as a method of determining the optimal classification plane with minimum error, is defined as Equation (8). Furthermore, LDA, which involves determining the W that maximizes the rate between classes and minimizes the rate within each class is defined as Equations (10)- (12).…”

Section: Window Removal and Identification Methodsmentioning

confidence: 99%

“…Such signals provide strong protection against forgery [5]. However, in biometric recognition using ECG, signal irregularities may exist due to the individual's illness, and the procedure could be hampered by difficulties, such as a long waiting time for data collection [6][7][8]. Nevertheless, studies on ECG biometrics can be used in extensive fields, such as commercial environments, security, health management, and systems like smart cards [9].…”

Section: Introductionmentioning

confidence: 99%

ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method

Jung¹,

Lee²

2017

Applied Sciences

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This study proposes electrocardiogram (ECG) identification based on non-fiducial feature extraction using window removal method, nearest neighbor (NN), support vector machine (SVM), and linear discriminant analysis (LDA). In the pre-processing stage, Daubechies 4 is used to remove the baseline wander and noise of the original signal. In the feature extraction and selection stage, windows are set at a time interval of 5 s in the preprocessed signal, while autocorrelation, scaling, and discrete cosine transform (DCT) are applied to extract and select features. Thereafter, the window removal method is applied to all of the generated windows to remove those that are unrecognizable. Lastly, in the classification stage, the NN, SVM, and LDA classifiers are used to perform individual identification. As a result, when the NN is used in the Normal Sinus Rhythm (NSR), PTB diagnostic, and QT database, the results indicate that the subject identification rates are 100%, 99.40% and 100%, while the window identification rates are 99.02%, 97.13% and 98.91%. When the SVM is used, all of the subject identification rates are 100%, while the window identification rates are 96.92%, 95.82% and 98.32%. When the LDA is used, all of the subject identification rates are 100%, while the window identification rates are 98.67%, 98.65% and 99.23%. The proposed method demonstrates good results with regard to data that not only includes normal signals, but also abnormal signals. In addition, the window removal method improves the individual identification accuracy by removing windows that cannot be recognized.

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“…Face images can also be used to infer a wide set of soft biometric characteristics, such as the emotional state, ethnicity, gender, and age. Among this set of characteristics, the automatic age estimation can be particularly important in different scenarios [3], such as security and defense applications [4], surveillance [5], health-care systems [6], [7], entertainment [8], automated…”

Section: Introductionmentioning

confidence: 99%

Age estimation based on face images and pre-trained convolutional neural networks

Anand

Labati

Genovese

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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Age estimation based on face images plays an important role in a wide range of scenarios, including security and defense applications, border control, human-machine interaction in ambient intelligence applications, and recognition based on soft biometric information. Recent methods based on deep learning have shown promising performance in this field. Most of these methods use deep networks specifically designed and trained to cope with this problem. There are also some studies that focus on applying deep networks pre-trained for face recognition, which perform a fine-tuning to achieve accurate results. Differently, in this paper, we propose a preliminary study on increasing the performance of pre-trained deep networks by applying postprocessing strategies. The main advantage with respect to finetuning strategies consists of the simplicity and low computational cost of the post-processing step. To the best of our knowledge, this paper is the first study on age estimation that proposes the use of post-processing strategies for features extracted using pretrained deep networks. Our method exploits a set of pre-trained Convolutional Neural Networks (CNNs) to extract features from the input face image. The method then performs a feature level fusion, reduces the dimensionality of the feature space, and estimates the age of the individual by using a Feed-Forward Neural Network (FFNN). We evaluated the performance of our method on a public dataset (Adience Benchmark of Unfiltered Faces for Gender and Age Classification) and on a dataset of nonideal samples affected by controlled rotations, which we collected in our laboratory. Our age estimation method obtained better or comparable results with respect to state-of-the-art techniques and achieved satisfactory performance in non-ideal conditions. Results also showed that CNNs trained on general datasets can obtain satisfactory accuracy for different types of validation images, also without applying fine-tuning methods.

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Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

Cited by 14 publications

References 36 publications

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

ECG-Based Subject Identification Using Common Spatial Pattern and SVM

ECG Identification Based on Non-Fiducial Feature Extraction Using Window Removal Method

Age estimation based on face images and pre-trained convolutional neural networks

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