Comparison Among Physiological Signals for Biometric Identification

The biometric is an open research field that requires analysis of new techniques to increase its accuracy. Although there are active biometric systems for subject identification, some of them are considered vulnerable to be fake such as a fingerprint, face or palm-print. Different biometric studies based on physiological signals have been carried out. However, these can be regarded as limited. So, it is important to consider that there is a need to perform an analysis among them and determine the effectivity of each one and proposed new multimodal biometric systems. In this work is presented a comparative study of 40 physiological signals from a multimodal analysis. First, a preprocessing and feature extraction was carried out using Hermite coefficients, discrete wavelet transform, and statistical measures of them. Then, feature selection was applied using two selectors based on Rough Set algorithms, and finally, classifiers and a mixture of five classifiers were used for classification. The more relevant results shown an accuracy of 97.7% from 3 distinct EEG signals, and an accuracy of 100% using 40 different physiological signals (32 EEG, and eight peripheral signals).