An efficient palmprint biometric system based on progressive image compression

In this paper, we propose an efficient multi-spectral palmprint identification system. For that, we compressed each palmprint band at 0.5 bit per pixel (bpp) by a progressive image compression algorithm where we used the famous Set Partitioning In Hierarchical Trees (SPIHT ) coder. By exploiting the progressiveness of this algorithm, we obtained three images decompressed in several times with different resolutions. One image decompressed at 0.25 bpp which represents the image approximation, the other at 0.25 bpp which represents the edges or details and the third is the global of them. So, we applied a binarization scheme and the Gabor algorithm for the last two images respectively for modeling. Subsequently, all bands are integrated in order to construct an efficient multimodal identification system based on matching score level fusion. Finally, Experimental results show that our proposed scheme yields excellent performance for identifying palmprints.