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Biometrics are essentially signal features extracted from the human body for a number of purposes: identi cation, authentication, or providing network security
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Electrocardiogram (ECG) Biometric for Robust Identi cation
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[3, 5, 9]. However, not every physiological or behavioral attribute is appropriate for biometric use. For instance, to be conducive to a biometric construction, the trait should be universal (present in all human beings) and yet distinct (unique for an individual) [9]. To date, the human features commonly used as biometrics comprise: ngerprint, iris, face, voice, gait, or even keystroke dynamics. By comparison, the ECG biometric represents a more recent development in biometric research. In this section, an overview of existing biometric methods and applications using the ECG will be presented.
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ECG-Based Identification Using Fiducial Points
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Many of the methods currently found in the ECG biometric literature can be categorized as ducial techniques. Among the earliest works in the area is Biel et al. s [15] proposal for a ducial feature extraction algorithm, which demonstrated the feasibility of using ECG signals for human identi cation. A Siemens ECG device was used to record cardiovascular signals from 20 subjects. This apparatus was also employed for feature extraction. The feature space consisted of temporal and amplitude distances of speci c heartbeat points. Further analysis was performed by analyzing the correlation matrix, for dimensionality reduction of feature vectors. A 100% subject identi cation rate was achieved with this methodology, for subjects of various ages. Israel et al. [16] introduced an ECG-based identi cation system for temporal features extraction. According to these authors, when a subject arrives to the system to be identi ed, the input ECG was ltered to eliminate noise effects. The next step was to detect the peaks in the time domain by nding local maxima in regions surrounding each of the P, R, and T complexes. A total of 15 time duration features were then extracted from each heartbeat. Wilks Lamda was employed to select a set of characteristics from the feature space, followed by linear discriminant analysis for classi cation. The system achieved 100% subject and 81% heartbeat recognition rate for 29 subjects. In a later work by Israel et al. [17], a framework that fused face and ECG traits was reported. In this multimodal biometric method, the ECG signal analysis and feature extraction procedures are similar to those in reference 16. Shen et al. [18] reported a two-stage framework for identity veri cation using one-lead ECG signals. During the rst step, template matching was applied to compute the correlation coef cient among the QRS complexes from the gallery set, which can be considered as candidates for a signal. A decision-based neural network (DBNN) was then used to nalize the veri cation from the possible candidates selected with template matching. This type of methodology achieved a veri cation rate of 95% for template matching, 80% for the DBNN, and 100% for integrating the two methods. The suggested methodology was extended by Shen [19] in a larger database, containing 168 healthy subjects. The highest identi cation rate achieved in that work was 95.3%. Wang et al. [20] proposed an integration of analytic and appearance-based features from heartbeats. The ducial points of ECG signals were detected in the
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16.3 The ECG as a Biometric: A Literature Survey
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preprocessing step, in order to to extract temporal and amplitude distances to form a feature vector. The classi cation performance demonstrated that even though amplitude features have discriminative ability, analytic features in general are not suf cient for identity recognition. The experimentation described in reference 20 involved extraction of appearance-based features with the help of either the principal component or linear discriminant analysis. When these two types of features were fused in a hierarchical scheme, a 100% subject and 98.9% heartbeat identi cation rates were achieved for 13 subjects. W bbeler et al. [21] addressed the issue of uctuating anxiety states that affected u the heart rate. Instead of extracting features from a heartbeat, the morphology of the QRS complex is utilized for feature extraction this complex being less susceptible to rhythm variance [22]. The selected features included a two-dimensional heart vector, with embedded information from three leads, and the corresponding rst and second temporal derivatives. When the method was tested on ECGs from 74 subjects, a 99% identi cation rates was achieved.
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