Fingerprint Image Compression using Biorthogonal and Orthogonal Wavelets at Different Levels of Discrete Wavelet Transform

Abstract

Storage space has become a significant issue in this digital generation. Forensic applications such as criminal investigations, terrorist identification, and national security issues require large amounts of space to store large volumes of fingerprints and face recognition scans collected and stored every day. Ultimately, less memory space results to more time used to process and transmit an image. In this paper, fingerprint images are compressed using the DWT (discrete wavelet transform) biorthogonal wavelet Bior4.4 and orthogonal wavelet Haar at different levels of transformation. The experiment is recognized through python and is divided into two parts; transformation testing and SPIHT (Set Partitioning in Hierarchical Trees) algorithm compression. The peak signal to noise ratio (PSNR), mean square error (MSE) as well as the compression ratio (CR) are used to objectively enumerate the quality of the compressed images. It is observed that MSE and PSNR are favorable when transformation is done using Bior 4.4 first then Haar during the remaining levels as proposed rather than using one distinct wavelet for transformation.