Biometric Identification System Based on Electrocardiogram Data

Abstract

Recent advancements in computing and digital signal processing technologies have made automated identification of people based on their biological, physiological, or behavioral traits a feasible approach for access control. The wide variety of available technologies has also increased the number of traits and features that can be collected and used to more accurately identify people. Systems that use biological, physiological, or behavioral trait to grant access to resources are called biometric systems. In this paper we present a biometric identification system based on the Electrocardiogram (ECG) signal. The system extracts 24 temporal and amplitude features from an ECG signal and after processing, reduces the set of features to the nine most relevant features. Preliminary experimental results indicate that the system is accurate and robust and can achieve a 100% identification rate with the reduced set of features.

Biometric Applications of One-Dimensional Physiological Signals - Electrocardiograms

“…This section provides a detailed review of research geared toward the usefulness of ECGs as biometric indicators. Since the first such effort was reported in 1999 (Biel, et al, 1999), approximately 20 different groups have researched this interesting area (Israel, et al, 2005;Chan, et al, 2008;Biel, et al, 2001;Zhang & Wei, 2006;Biel, et al, 1999;Boumbarov, et al, 2009;Chan, et al, 2006;Chiu, et al, 2008;Fatemian & Hatzinakos, 2009;Gahi, et al, 2008;Israel, et al, 2003;Kim, et al, 2005;Kyoso, 2003;Kyoso & Uchiyama, 2001;Micheli-Tzanakou, et al, 2009;Nasri, et al, 2009;Plataniotis, et al, 2006;Shen, et al, 2002;Sufi & Khalil, 2008;Wang, et al, 2006;Yao & Wan, 2010) -see Table 1. Most of these published works use a similar approach to present research findings.…”

“…• ECG Data Sources: Multiple groups experimentally acquired ECG data from volunteers (Biel, et al, 1999(Biel, et al, 2001Chan, et al, 2006;Chan, et al, 2008;Gahi, et al, 2008;Israel, et al, 2005;Israel, et al, 2003;Kim, et al, 2005;Kyoso, 2003;Kyoso & Uchiyama, 2001;Sufi & Khalil, 2008;Wang, et al, 2006;.…”

“…However, few report complete demographic or health-condition information for participants. Furthermore, the time interval between subject enrollment and data collection, a critical element when determining the effectiveness of a biometric modality, is frequently overlooked (Chiu, et al, 2008;Gahi, et al, 2008;Israel, et al, 2005;Kim, et al, 2005;Kyoso, 2003;Plataniotis, et al, 2006;Saechia, et al, 2005;Sufi & Khalil, 2008). Even studies that record this information often mention it vaguely (Chan, et al, 2008;Fatemian & Hatzinakos, 2009;Singh & Gupta, 2008) (see Table 1).…”

“…• Selection of Classification Features: Most investigators assess time domain features (e.g., time intervals between P, Q, R, S, and T waves, along with their amplitudes) (Biel, et al, 2001;Boumbarov, et al, 2009;Gahi, et al, 2008;Israel, et al, 2005;Kyoso, 2003;Z. Zhang & Wei, 2006) and angle information (Singh & Gupta, 2008).…”

ECG-based identity recognition via deterministic learning

“…However, its security level is very limited, as it can be cracked easily, even though variable password policies (e.g. mixture of lowercases and uppercase letters, periodic scheduled password changes) have been introduced [1]. To achieve a higher security level, more complex security systems have been invented based on human biometric traits (e.g.…”