Verifikasi dan Identifikasi Telapak Tangan dengan Kernel Gabor
Abstract
In recent years, biometric recognition has been rapidly developed and still continues to grow. Researchers are combining several algorithms to obtain a more robust feature. In this study, Gabor kernel methods, principle component analysis (PCA), detection error trade-off (DET), expected performance curves (EPC), and cumulative match characteristic (CMC) is combined and used to obtain the features of palm print. This experiment shows that the combination of Gabor and PCA methods, using 240 items of data, gives an optimum result in palms identification and authentication.
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