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PASSOS, Henrique dos Santos. Ensemble of symbolic representation techniques for biometric recognition based on ECG signals. 2018. 103 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2018. Identification people methods have been very important for the whole society. Currently, research on biometrics have been widely encouraged by various sectors of the industry worldwide in order to improve or replace existing security systems and people identification. The field of biometrics includes a variety of technologies used to identify or verify the identity of a person by measuring and analyzing various physical and/or behavioral aspects of the human being. Several biometric methods have been proposed for recognition of people, such as fingerprint, iris, face and speech. These biometric modalities have different characteristics in terms of performance, measurability and acceptability. One issue to be considered with the biometric application in the real world is its robustness to attacks by circumvention, repetition and obfuscation. These attacks are becoming more frequent and more questions are being raised about the levels of security that this technology can offer. Biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG) and electromyogram (EMG) have been increasingly studied and applied to biometric recognition. Specifically, ECG signals have been widely adopted for biometric recognition in various works. On the other hand, time series analysis has been used successfully in many different applications to identify temporal patterns in the data. Although simple dynamics can be observed with traditional analytical tools such as fourier transform, wavelet transform, the symbolic representation can improve the analysis of processes that are complex and possibly chaotic. In addition, symbolic representation can also reduce noise sensitivity and greatly improve computational efficiency. However, there are structural and parametric design aspects that can lead to performance degradation. In the absence of a systematic and inexpensive methodology for proposing optimally specified symbolic representation techniques, machine committees, more specifically ensemble, present themselves as promising alternatives. In this study, the components of the committee, which correspond to techniques of symbolic representation, and their respective parameters were selected via evolutionary algorithms. The objective is to jointly explore the potentialities of both symbolic representation techniques and machine committee for biometric recognition based on ECG signals. Experimental results conducted on two publicly available datasets indicate that the proposed approach may improve recognition performance when compared to traditional techniques.

Ensemble de técnicas de representação simbólica para reconhecimento biométrico baseado em sinais de ECG