Large volumes of fingerprints are collected and stored every day in a wide range of applications, including forensics, access control etc., and are evident from the database of Federal Bureau of Investigation (FBI) which contains more than 70 million finger prints. Wavelet based Algorithms for image compression are the most successful, which result in high compression ratios compared to other compression techniques. Even though wavelet bases are providing good compression ratios, they are not optimal for representing images consisting of different regions of smoothly varying grey-values, separated by smooth boundaries. This issue is addressed by the directional transforms, known as contourlets which have the property of preserving edges. This paper focuses mainly on the new fingerprint compression using contourlet transform (CT), which includes elaborated repositioning algorithm for the CT coefficients, and Modified set partitioning in hierarchical trees (SPIHT) which is applied to get better quality, i.e., high peak signal to noise ratio (PSNR). The results obtained are tabulated and compared with those of the wavelet based ones.

This paper presents a new fingerprint coding technique using non-linear approximation of multiwavelet coefficients followed by multi-stage vector quantization. Fingerprints contain oscillatory patterns in the vertical, horizontal and diagonal direction. Wavelet transform often fails to accurately capture the oscillatory patterns in the fingerprints, especially at low bit rates. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for efficient image compression. New class of wavelets called ‘Multiwavelets’ which posses more than one scaling function overcomes this problem. The effective multiwavelet coefficients are quantized by multistage vector quantization which can achieve very low encoding and storage complexity in comparison to unstructured vector quantization. Entropy coding of quantized coefficients is done using Huffman coding. The performance of the proposed scheme is compared with the results obtained from scalar wavelets. The performance of the proposed scheme is better than the existing wavelet based coding at low bit rate.

This paper presents a new coding technique for fingerprint compression, which is based on contourlet transform and Self-Organizing Feature Map (SOFM) vector quantization. One of the main difficulties in developing compression algorithms for fingerprints resides in the need for preserving the minutiae, which are subsequently used in identification. Wavelets have shown their ability in representing natural images that contain smooth areas separated with edges. However, wavelets cannot efficiently represent the ridge and furrow patterns, which are predominant in fingerprints. This issue is addressed by directional transforms, known as contourlets, which have the property of preserving edges. SOFM based vector quantizer quantizes the coefficients obtained by contourlet transform. The results obtained are tabulated and compared with those of the wavelet based ones.