Identification of multi-spectral palmprints using energy compaction by Hybrid wavelet

Orthogonal Transforms and Wavelets can be used to extract features of a biometric in frequency domain. They also exhibit the property of energy compaction which can be used to select few coefficients as features of an image. Here we make use of a Hybrid wavelet, generated by using Kronecker product of two existing orthogonal transforms, Walsh and DCT to identify multi-spectral palmprints. A threshold energy value is chosen to select all coefficients whose total energy is above that value. One-to-many identification on a large database containing 3 sets of 6000 multi-spectral palmprint images from 500 different palms is used to validate the performance of the proposed method and matching accuracy in terms of genuine acceptance ratio of 99.979% using score level fusion has been obtained. Hence this method can significantly improve the identification rates for palmprint images.