Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Wavelets in ECG Security Application

Wavelet packet transform has been used in many applications of biomedical signal processing, for example, feature extraction, noise reduction, data compression, electrocardiogram (ECG) anonymisation and QRS detection. The wavelet analysis methods, in these applications, represent the temporal characteristics of a biological signal by its spectral components in the frequency domain. Furthermore, it has been shown in many works that the ECG signal can be used as a biometric method for robust human identification and authentication. In this case, it is necessary to anonymise the ECG data during the distribution and storage of the signal in a public repository. A neglectful system leads to an eavesdropper recording the ECG data and uses it as recognition data to gain access via an ECG biometric system. This chapter discusses and reviews recent researches on ECG anonymisation wavelets-based techniques. These techniques use discrete wavelet transform and wavelet packet transform. A comparative study between the wavelets-based methods will be presented

Cardioid graph based ECG biometric in varying physiological conditions using compressed QRS

This paper proposes a robust biometric identification system using compressed electrocardiogram (ECG) signal by varying physiological conditions. The ECG data were obtained by recording a total of 30 healthy subjects where they performed six regular daily activities repeatedly at a sampling frequency of 1000 Hz. Then, the QRS complexes are segmented by implementing Amplitude Based Technique (ABT) where it compares the amplitudes of ECG points to determine the R peak. The segmented QRS is then compressed for various levels by using Discrete Wavelet Transform (DWT) algorithms and first 3 Daubechies (db) wavelet are computed. Next, a Cardioid graph is generated. In order to verify the matching process, the classification is performed by using the Multilayer Perceptron (MLP) technique. The results show that by applying this method, the accuracy of the identification rate can be achieved as high as 96.4% even when the data file is compressed up to 73.3%. When the data file is compressed, the outcomes also demonstrate that the execution time is less compare to non-compressed data. Therefore, the biometric identification system can be implemented efficiently as there will be a lesser issue regarding the data storage, execution time and accuracy based on the outcome of the study

Intelligent Person Recognition System Based on ECG Signal

Automated recognition based on ECG signal is now preferable to identify the person for security monitoring work. This approach is gradually replacing manual techniques that claimed to be outdated. However, it is challenging task to execute the automated system since it is in the infant stage. In this paper, an intelligent person recognition system based on ECG signal is proposed. Here, 79 recorded signals from 79 subjects are used. Three processes are i.e. pre-processing, feature extraction and classification is discussed. A combination of enhanced start and end point detection namely short time energy (STE) and short time average zero crossing rate (STAZCR) is employed in the pre-processing. Subsequently, an autocorrelation method is applied in feature extraction. Finally, support vector machine (SVM) is implemented to evaluate the performance of the system. The experimental demonstrate that 0.93% to 0.97% equal error rate (EER) is achieved when the training data is set to 35

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Identifying Humans by the Shape of Their Heartbeats and Materials by Their X-Ray Scattering Profiles

Security needs at access control points presents itself in the form of human identification and/or material identification. The field of Biometrics deals with the problem of identifying individuals based on the signal measured from them. One approach to material identification involves matching their x-ray scattering profiles with a database of known materials. Classical biometric traits such as fingerprints, facial images, speech, iris and retinal scans are plagued by potential circumvention they could be copied and later used by an impostor. To address this problem, other bodily traits such as the electrical signal acquired from the brain (electroencephalogram) or the heart (electrocardiogram) and the mechanical signals acquired from the heart (heart sound, laser Doppler vibrometry measures of the carotid pulse) have been investigated. These signals depend on the physiology of the body, and require the individual to be alive and present during acquisition, potentially overcoming circumvention. We investigate the use of the electrocardiogram (ECG) and carotid laser Doppler vibrometry (LDV) signal, both individually and in unison, for biometric identity recognition. A parametric modeling approach to system design is employed, where the system parameters are estimated from training data. The estimated model is then validated using testing data. A typical identity recognition system can operate in either the authentication (verification) or identification mode. The performance of the biometric identity recognition systems is evaluated using receiver operating characteristic (ROC) or detection error tradeoff (DET) curves, in the authentication mode, and cumulative match characteristic (CMC) curves, in the identification mode. The performance of the ECG- and LDV-based identity recognition systems is comparable, but is worse than those of classical biometric systems. Authentication performance below 1% equal error rate (EER) can be attained when the training and testing data are obtained from a single measurement session. When the training and testing data are obtained from different measurement sessions, allowing for a potential short-term or long-term change in the physiology, the authentication EER performance degrades to about 6 to 7%. Leveraging both the electrical (ECG) and mechanical (LDV) aspects of the heart, we obtain a performance gain of over 50%, relative to each individual ECG-based or LDV-based identity recognition system, bringing us closer to the performance of classical biometrics, with the added advantage of anti-circumvention. We consider the problem of designing combined x-ray attenuation and scatter systems and the algorithms to reconstruct images from the systems. As is the case within a computational imaging framework, we tackle the problem by taking a joint system and algorithm design approach. Accurate modeling of the attenuation of incident and scattered photons within a scatter imaging setup will ultimately lead to more accurate estimates of the scatter densities of an illuminated object. Such scattering densities can then be used in material classification. In x-ray scatter imaging, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume. A mask placed between the object and the detector array provides information about scatter angles. An efficient computational implementation of the forward and backward model facilitates iterative algorithms based upon a Poisson log-likelihood. The design of the scatter imaging system influences the algorithmic choices we make. In turn, the need for efficient algorithms guides the system design. We begin by analyzing an x-ray scatter system fitted with a fanbeam source distribution and flat-panel energy-integrating detectors. Efficient algorithms for reconstructing object scatter densities from scatter measurements made on this system are developed. Building on the fanbeam source, energy-integrating at-panel detection model, we develop a pencil beam model and an energy-sensitive detection model. The scatter forward models and reconstruction algorithms are validated on simulated, Monte Carlo, and real data. We describe a prototype x-ray attenuation scanner, co-registered with the scatter system, which was built to provide complementary attenuation information to the scatter reconstruction and present results of applying alternating minimization reconstruction algorithms on measurements from the scanner