On The Power of Joint Wavelet-DCT Features for Multispectral Palmprint Recognition
This incremental improvement enhances biometric security systems for identification applications.
The paper tackled palmprint identification by combining DCT and wavelet features, achieving near-perfect accuracy of 99.97-100% on a multispectral database, outperforming prior methods.
Biometric-based identification has drawn a lot of attention in the recent years. Among all biometrics, palmprint is known to possess a rich set of features. In this paper we have proposed to use DCT-based features in parallel with wavelet-based ones for palmprint identification. PCA is applied to the features to reduce their dimensionality and the majority voting algorithm is used to perform classification. The features introduced here result in a near-perfectly accurate identification. This method is tested on a well-known multispectral palmprint database and an accuracy rate of 99.97-100\% is achieved, outperforming all previous methods in similar conditions.