Highly Accurate Multispectral Palmprint Recognition Using Statistical and Wavelet Features
This work addresses personal recognition systems, offering a highly accurate biometric solution, but it appears incremental as it builds on existing feature extraction and classification techniques.
The paper tackled palmprint recognition by proposing a method using statistical and wavelet features with two classification approaches, achieving an accuracy rate of 99.65%-100% on a dataset of 6000 samples.
Palmprint is one of the most useful physiological biometrics that can be used as a powerful means in personal recognition systems. The major features of the palmprints are palm lines, wrinkles and ridges, and many approaches use them in different ways towards solving the palmprint recognition problem. Here we have proposed to use a set of statistical and wavelet-based features; statistical to capture the general characteristics of palmprints; and wavelet-based to find those information not evident in the spatial domain. Also we use two different classification approaches, minimum distance classifier scheme and weighted majority voting algorithm, to perform palmprint matching. The proposed method is tested on a well-known palmprint dataset of 6000 samples and has shown an impressive accuracy rate of 99.65\%-100\% for most scenarios.