Fingerprint Image Compression Using Wavelet Transform
Hanashi, Abdalla Musbah (2003) Fingerprint Image Compression Using Wavelet Transform. Masters thesis, Universiti Putra Malaysia.
The fingerprint is considered to be the most reliable kind of personal identification because it cannot be forgotten, misplaced, or stolen. Fingerprint authorization is potentially the most affordable and convenient method of verifying a person's identity. Storage of fingerprint image databases needs allocation of huge secondary storage devices. To reduce the increasing demand on storage space, efficient data compression techniques are needed. In addition to that, the exchange of fingerprint images between the governmental agencies could be done fast. The compression algorithm must also preserve original information in the original image. Digital image compression based on the ideas of subband decomposition or discrete wavelet transform (DWT) has received much attention in recent years. In fact, wavelet refers to a set of basic function, which is recursively defined form, a set of scaling coefficients and scaling function. Discrete Wavelet Transform CDWT) represents images as a sum of wavelet function on different resolution level. Essential for wavelet transform can be composed of any function that satisfies requirements of multi-resolution analysis. It means that there exists a large selection of wavelet families depending on choice of wavelet function. The objective of this study is to evaluate a variety of wavelet filters using Wavelet toolbox for selecting the best wavelet filters to be used in compress and decompress of selected fingerprint images. Therefore a two-dimensional wavelet decomposition, quantization and reconstruction using several families of filter banks were applied to a set of fingerprint images. The results show that no specific wavelet filter performs uniformly except for Biorthogonal and Symlets, and that is using the matching technique. The result shows that at a threshold value equal of 160 and decomposition level 3 with a wavelet filter sym4, there is no difference between the original and reconstructed image. This study concludes that using wavelet filters sym4 and bior3.7 can achieve compression ratio 27: 1 with PSNR 20.36 dB and 17: 1 with PSNR 21.88 dB respectively. These values indicate that using these filters, the quality of the reconstructed fingerprint still exist.
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