Human Identification Based on ECG and Palmprint
Abstract
In this paper, a new approach in human identification is investigated. For this purpose, we fused ECG and Palm print biometrics to achieve a multimodal biometric system. In the proposed system for fusing biometrics, we used MFCC approach in order to extract features of ECG biometric and PCA to extract features of Palm print. The features undergo a KNN classification. The performance of the algorithm is evaluated against the standard MIT-BIH and POLYU databases. Moreover, in order to achieve more realistic and reliable results, we gathered Holter ECG recordings acquired from 50 male and female subjects in age between 18 and 54. The numerical results indicated that the algorithm achieved 94.7% of the detection rate.
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