Palmprint characterization using multi-wavelet transform for human identifiction

Abstract EN

The human hand presents the source for a numerous of physiological biometric features, from these are palmprint, hand geometry, finger geometry and the vein pattern on the dorsum of the hand, are mostly used in many fields for different applications.

Lines and points are extracted from palms for individual identification in original image or frequency space.

In this paper, a preprocessing to extract the central part from the input palmprint image, next a 2-D multi-wavelet transform is used to convert the palmprint image into 16 sub-bands, and the texture feature vectors, energy and entropy for each of the 16 sub-bands is computed and normalized with min-max method for individual identification.

The correlation distance is used as a similarity measure.

The experimental results point up the effectiveness of a method in either using low resolution or noisy images.