ECG based biometric human identification using chaotic encryption

Analysis of recent studies and publications. The analysis of the modern market of software and algorithmic solutions for performing the Liveness detection process showed that the currently existing approaches are completely commercial solutions with closed algorithms of their work. In addition, the Liveness detection algorithm is not yet standardized, and has many implementation options that can sometimes lead to am-biguous results [7]. That is why, it was decided to develop our own algorithm and liveness detection system based on obtaining face characteristics using the AWS API [8], because this service offers a high accuracy of face recognition, which is 99.99%, and provides 10,000 free requests to use every month, which enough to fulfill the purpose of our work. Purpose of the study. Development of the algorithm for the Liveness Detection process using AWS and the construction of a video identification system based on it. Main research material. This paper investigates the video identification of a per-son using the Liveness Detection process. The basis of the question, complexity of the Liveness Detection process and the implementation of biometric human video identifica-tion have been studied. An algorithm for conducting the Liveness Detection process based on the execution of random tasks by an identified person has been developed. Integration with the Amazon DetectFaces API was carried out in order to obtain the character of a digital image of the head, as a result of which it becomes possible to analyze a photo of a person for a wide range of key facial features. A manual test of the Amazon DetectFaces API was conducted, in the process of which, empirically, thresh-olds of facial characteristics in the image for the Liveness Detection process were set. Integration with the Amazon CompareFaces API was implemented to execute the bio-metric video identification. The testing of the developed Liveness Detection application system based on the created Liveness passing algorithm and selected AWS API thresholds consisted of 100 tests of different orientations, from direct user-flow testing to tests with a medical mask, glasses and beard, a photo of a face and a video image of a person for the purpose of forgery his personality. Thanks to the initial accuracy of Amazon API facial recognition of 99.99% and the random selection of tasks in the developed Liveness Detection algo-rithm, all tests gave the expected result - the person was either successfully video-identified at the enterprise, or his identity was faked during the Liveness Detection pro-cess. Conclusions. Based on the research, the software system of biometric human video identification has been developed, which allows to automatically establish the presence of a person in front of a video camera and identify it, preventing the use of fake artifacts to falsify a person's identity.

In the current paper a generalized net model of the biometric identification is presented. The modeled process contains combination of the steps included in unimodal, multibiometric and hybrid identification systems. The constructed generalized net model can be used for analyzing and simulating the processes included in biometric identification systems. It can be helpful in procedures of managing and optimizing the biometric identification processes. In the second part of the paper a MATLAB implementation of the biometric fingerprint identification is presented.

Background. In recent years, the development of an automated identification process using biometric authentication has been observed, which has a high level of protection, since it allows you to evaluate the physical parameters and characteristics of a particular person. Such access control is more reliable since identifiers cannot be transferred to third parties or duplicated to bypass security systems. Over the past decades, a significant number of systems with biometric identification has been developed, but systems with identification according to the characteristics of the photoplethysmogram still receive little attention. The main task of biometric personality identification using photoplethysmogram is the search and implementation of machine learning methods to determine their belonging to a particular patient.Objective. The purpose of the paper is to develop an algorithm for distinguishing pulse wave iterations using the calculation of the temporal characteristics of photoplethysmograms, such as the maximum amplitude value, variance, mean absolute deviation, Wilson amplitude and the total sum of signal amplitude values.Methods. Based on the study of the temporal characteristics of the photoplethysmogram, an algorithm for distinguishing pulse wave iterations is created, which can be used for further biometric identification of a person using machine-learning methods.Results. The results can be used for further development of automated access control and management systems using biometric identification.Conclusions. Known methods of biometric identification are usually based on the static parameters of a person (the structure of the cornea of the eye, palm, fingerprints, geometry of the auricle, etc.), but have a low level of protection, since using special equipment you can create a copy of the biometric key. Therefore, today, the use of methods based on the parameters of dynamic biometric identification (plethysmogram, cardiogram and others) provides the highest degree of protection, but requires a more accurate software device to isolate and determine common symptoms. The proposed approach to calculating individual parameters of the photoplethysmogram with the aim of their subsequent classification by machine learning methods may be an acceptable solution for patient biometric identification systems.

Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

Biometric recognition systems or human identification are very important in security access for identification and verification systems. The biometric recognition system can be used as an identification system based on the characteristics possessed by the body part of each individual. The soles of the feet can be used for identification because the soles of the feet have certain and unique characteristics which include major lines, protrusions, small dots, single points, and textures. The introduction of biometrics in babies is still conventional, which is a standard operating procedure such as attaching bracelets on baby's feet and imprinting or inking on the soles of baby's feet which are affixed to paper and are very vulnerable to the risk of damage or loss of data, there is a need for a system that can store data automatically digital and able to do the baby identification process. The Principal Component Analysis method is used for the extraction process of the characteristics of the baby's feet. The classification uses the K-Nearest Neighbor (K-NN) method with the euclidean distance approach. Tests were carried using 120 images of baby feet, there are 20 classes, each class contains 3 images of the right foot and 3 images of the foot of the left foot, and a dataset of 280 training images. The highest accuracy result obtained in system testing is 91% with a computation time of 5.63 seconds using the Principal Component Analysis method with the K-Nearest Neighbor (K-NN) classification.Keywords: Footprint, Feature Extraction, Principal Component Analysis, K-Nearest Neighbor.

Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.

Biometric identification has gained a large popularity in recent years. Biometric identification of a person is giving more accurate results comparing to other security methods. In this paper we have proposed a method to recognize a person by the way he walks. This paper mainly focuses on moving subject (human) identification using Gaussian filter, background reduction, joint point plotting and feature mapping, vectorization. We have utilized the CASIA dataset. We have implemented an algorithm which is a mapping method where the generated silhouettes are mapped with point and eclipse on joints and other body parts. We have used Relevance vector machine which is a machine learning technique which use Bayesian interface to produce results for classification. With RVM we have classified and identified the person using feature extracted from given algorithm.

Biometric identification is thought to be less vulnerable to fraud and forgery than are traditional forms of identification. However biometric identification is not without vulnerabilities. In a 'spoofing attack' an artificial replica of an individual's biometric trait is used to induce a system to falsely infer that individual's presence. Techniques such as liveness-detection and multi-modality, as well as the development of new and emerging modalities, are intended to secure biometric identification systems against such threats. Unlike biometrics in general, the societal and ethical issues raised by spoofing and anti-spoofing techniques have not received much attention. This paper examines these issues.

In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust.

The paper explores the challenge of human identification through gait recognition within biometric identification systems. Itoutlines the essential criteria for human biometric features, discusses primary biometric characteristics, and their application in biometric identification systems. The paper also examines the feasibility of utilizing gait as a biometric identifier, emphasizing its advantages, such as not requiring the upfront provision of personal biometric information and specialized equipment.The authors conduct an analysis of existing scientific literature in the field of gait recognition, categorizing gait recognition methodsinto template-based and non-template-based approaches. Throughout their research, they identify the key issues and challenges that researchers face in this domain, along with the prevailing trends in human gait recognition within biometric identification systems.Additionally, the paper introduces a method for person identification based on gait, utilizing the Histogram of Oriented Gradients and the Sum Variance Haralick texture features. It involves transforming input video into a series of images depicting the gait silhouette, creating a Gait Energy Image (GEI) by combining these gait silhouettes throughout a gait cycle, and translating the GEI into the Gait Gradient Magnitude Image (GGMI). The subsequent step involves extracting recommended gait characteristics from the GGMIs of participants included in a dataset.To preprocess the collected characteristics, Principal Component Analysis (PCA) is applied, reducing the dimensions that may negatively impact classification robustness, thereby enhancing overall performance. In the final step, a K-Nearest Neighbors (KNN) classifier is employed to categorize the characteristics obtained from a specific dataset.The proposed novel feature vector in the paper demonstrates increased reliability and effectively captures spatial variations in gait patterns. Notably, it reduces the dimensionality of the feature vector from 3780×1 to 63×1, resulting in decreased computational complexity in the gait recognition system. Experimental evaluations on the CASIA A and CASIA B datasets reveal that the proposed approach outperforms other HOG-based methods in most scenarios, with the exception of situations involving frontal images.

An intelligent computer-aided diagnosis method for paroxysmal atrial fibrillation patients with nondiagnostic ECG signals

This paper proposes an approach for R-peak detection employing various signal processing techniques and abnormality detection in ECG signals involving classification into heart disorder categories like different arrhythmias and premature ventricular contractions using 2D Scalograms for Continuous Wavelet Transform (CWT) of signals and deep learning. For training the neural network, we have utilized the ECG Heartbeat Categorization Dataset from Kaggle online community. We utilized the ECG samples extracted from the MIT-BIH Arrhythmia Dataset and achieved a validation accuracy of 98.32%. The strategies employed for denoising the noisy ECG signal containing random noise, White Gaussian noise, 50 Hz AC Hum and high-frequency noise achieved a signal-to-noise ratio of 12.69 after filtering.

A robust embedded biometric authentication system based on fingerprint and chaotic encryption

In this article, we present the use of sparse representation of signal and dictionary learning method for solving the problem of anomaly detection. The analyzed signal was presented as a set of correct ECG structures and outliers (characterizing different types of disorders). In the course of learning we used the modified Method of Optimal Directions (MOD) to find a dictionary that would reflect correct structures of an ECG signal. The dictionary found this way became a basis for sparse representation of the analyzed ECG signal. In the process of anomaly detection based on decomposition of the analyzed signal onto correct values and outliers, there was used a modified Alternating Minimization Algorithm (AMA). Performance of the proposed method was tested using a widely available database of ECG signals - MIT–BIH Arrhythmia Database. The obtained experimental results confirmed the effectiveness of the method of anomaly detection in the analysed ECG signals.

AArrhythmia is an irregular in a person's beating heart that can happen occasionally. Heart rhythm problems can have disastrous results and seriously endanger health. Visually analyzing ECG data might be complex due to its large amount of information. Designing an automated method to assess the massive amount of ECG data is crucial. This research shows continuous wavelet transform (CWT) and deep learning strategies to automate detection and classification processes to examine three different ECG signals: congestive heart failure (CHF), normal sinus rhythm (NSR), and arrhythmia (ARR). CWT converts ECG signals into scalogram images for noise reduction and feature extraction. In deep learning, the modified SqueezeNet is employed to recognize the output of CWT, which is produced by the input of the ECG data. The proposed technique achieved 83.3%, 100%, and 94.7% accuracy in detecting CHF, NSR, and ARR. A comprehensive approach for classifying arrhythmias has been proposed, in which scalogram pictures of ECG waves are trained using the SqueezeNet model. The outcomes are superior to other current techniques and will significantly reduce wrong diagnoses