Real-Time ECG-Based Biometric Authentication System

Real-Time ECG-Based Biometric Authentication System

Jagannath Mohan, Adalarasu Kanagasabai, Vetrivelan Pandu
DOI: 10.4018/978-1-5225-8241-0.ch015
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Security plays an important role in present day situation where identity fraud and terrorism pose a great threat. Recognizing human using computers or any artificial systems not only affords some efficient security outcomes but also facilitates human services, especially in the zone of conflict. In the recent decade, the demand for improvement in security for personal data storage has grown rapidly, and among the potential alternatives, it is one that employs innovative biometric identification techniques. Amongst these behavioral biometric techniques, the electrocardiogram (ECG) is being chosen as a physiological modality due to the uniqueness of its characteristics which integrates liveness detection, significantly preventing spoof attacks. The chapter discusses the overview of existing preprocessing, feature extraction, and classification methods for ECG-based biometric authentication. The proposed system is intended to develop applications for real-time authentication.
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Biometric advances offer superior security systems over conventional authentication techniques, similar to secret word based ones, given the way that the biometric highlight could be a special physiological characteristic that continuously shows and, contingent upon the strategy utilized, may not be obvious to other individuals. In any case, one concern is that a few biometric strategies have certain equipment and reaction time prerequisites that make them improper for portable gadgets and cards (Boriev et al., 2015).

Finger impression may be a prevalent biometric method and has been utilized for over 100 a long time completely different applications, counting authentication on cell phones. The utilization of cards for monetary exchanges or secure get to has gotten to be irreplaceable within a recent couple of decades. This prominence has to been gone with by security concerns. Conventional cards don't bolster authentication and thus are not unequivocally related to their proprietor. Money related educate have attempted to address this issue through the presentation of PINs (Individual Confirmation Numbers) and incorporated circuits on cards. These highlights stay as it were valuable for contact cards. This has diminished the number of breaches, but detached assaults (Stick burglary or signature producing) are as yet hazardous (Poree et al., 2016).

Portable gadgets such as smartphones and PDAs have turned out to be crucial contraptions for various capacities. Clients are ending up more comfortable with putting away profoundly private data, for example, messages, photographs, and other delicate records on such gadgets. The well-known versatile login strategies depend on numerical or graphical secret codes. These systems are helpless against uninvolved assaults actuated by people observing from a distance to see the gadget screen or the tracking of the fingers with the objective of taking the secret code (Miakotko, 2017).

Conventional authentication has demerits as they can be spoofed by an assailant that captures the personality cleared out by clients on security. This has been illustrated with commercial frameworks that utilize finger impression authentication (Joy et al., 2016). Electrocardiogram (denoted to as ECG or EKG) techniques have the benefits of concealing the biometric highlights during authentication. Electrocardiography records the electrical activity of the heart over a specific timeframe with the help of electrodes placed on the human body (Biel et al., 2001). The electrodes locate the modest electrical changes from the body by heartbeats produced by the heart. There are three fundamental segments to an ECG (Louis et al., 2016). To begin with is the P wave, which acts the depolarization of atria, second is the QRS complex which acts the ventricular depolarization and final is T wave, which appears the ventricular repolarization. The ECG is separated into the following morphological features (Figure 1).

Figure 1.

The typical ECG waveform with its common intervals and point of measurement are depicted

  • PR Interval: Time difference between the beginning of the P and QRS wave.

  • P Wave: Corresponds to atrial depolarization.

  • PR Segment: Time difference between the beginning and end of the Q and P wave.

  • QRS Complex: Corresponds to ventricular depolarization.

  • ST Segment: Time difference between the beginning and end of T and S wave.

  • T Wave: Corresponds to ventricular repolarization.

  • QT Interval: Time difference between the QRS complex and the T wave.

Key Terms in this Chapter

Authentication: A process in which the credentials provided are compared to those on file in a database of authorized users’ information on a local operating system or within an authentication server. If the credentials match, the process is completed, and the user is granted authorization for access. The permissions and folders returned define both the environment the user sees and the way he can interact with it, including hours of access and other rights such as the amount of allocated storage space.

Biometry: A measurement and statistical analysis of people's physical and behavioral characteristics. The technology is mainly used for identification and access control, or for identifying individuals that are under surveillance. The basic premise of biometric authentication is that everyone is unique and an individual can be identified by his or her intrinsic physical or behavioral traits. (The term “biometrics” is derived from the Greek words “bio” meaning life and “metric” meaning to measure.)

Signal Acquisition: It is a process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems, abbreviated by the acronyms DAS or DAQ, typically convert analog waveforms into digital values for processing. The components of data acquisition systems include: Sensors converts physical parameters to electrical signals. Signal conditioning circuitry converts sensor signals into a form that can be converted to digital values. Analog-to-digital converters convert conditioned sensor signals to digital values.

Electrocardiography: The process of recording the electrical activity of the heart over a period of time using electrodes placed on a patient's body. These electrodes detect the tiny electrical changes on the skin that arise from the heart muscle depolarizing during each heartbeat. It is commonly a non-invasive procedure for recording electrical changes in the heart. The record, which is called an electrocardiogram, shows the series of waves that relate to the electrical impulses which occur during each beat of the heart. The results are printed on paper or displayed on a monitor. The waves in a normal record are named P, Q, R, S, and T and follow in alphabetical order. The number of waves may vary, and other waves may be present.

Template Matching: It is the act of checking a given sequence of features for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: “either it will or will not be a match.

Signal Processing: It concerns the analysis, synthesis, and modification of signals, which are broadly defined as functions conveying information about the behavior or attributes of some phenomenon. Signal processing techniques are used to improve signal transmission fidelity, storage efficiency, and subjective quality, and to emphasize or detect components of interest in a measured signal.

Feature Extraction: In machine learning, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is a dimensionality reduction process, where an initial set of raw variables is reduced to more manageable groups (features) for processing, while still accurately and completely describing the original data set.

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