No abstract

The aim of any fingerprint image compression technique is to achieve a maximum amount of compression with an adequate quality compressed image which is suitable for fingerprint recognition. Currently available techniques in the literature provide 100% recognition only up to a compression ratio of 180:1. The performance of any identification technique inherently depends on the techniques with which images are compressed. To improve the identification accuracy while the images are highly compressed, a multiwavelet-based identification approach is proposed in this paper. Both decimated and undecimated coefficients of SA4 (Symmetric Antisymmetric) multiwavelet are used as features for identification. A study is conducted on the identification performance of the multiwavelet transform with various sizes of images compressed using both wavelets and multiwavelets for fair comparison. It was noted that for images with size power of 2, the decimated multiwavelet-based compression and identification give a better performance compared to other combinations of compression/identification techniques whereas for images with size not a power of 2, the undecimated multiwavelet transform gives a better performance compared to other techniques. A 100% identification accuracy was achieved for the images from NIST-4, NITGEN, FVC2002DB3_B, FVC2004DB2_B and FVC2004DB1_B databases for compression ratios up to 520:1, 210:1, 445:1, 545:1 and 1995:1, respectively.

In this paper, a multi-wavelet transform with decimated frequency bands is proposed to be used in the Set Partitioning in Hierarchical Trees (SPIHT) algorithm to improve fingerprint image compression. Either shuffled or unshuffled multi-wavelets can be used for SPIHT algorithm. In both the cases, the quality of the compressed images at lower bit rates either remained the same or slightly improved compared to wavelets. To improve the performance at lower bit rates, a method which utilizes the decimated version of multi-wavelet for the initialization of lists in SPIHT algorithm is used. The multi-wavelet used for the proposed work is SA4 (Symmetric-Antisymmetric). The algorithm was tested and verified using NIST, Shivang Patel, NITGEN and other databases. An overall improvement in performance particularly at lower bit rates (0.01 to 0.09) compared to a multi-wavelet without decimation was obtained using this method. The improvement was 0.798dB, 0.857dB and 0.859dB for the images in NITGEN database for a multi-wavelet decimated by 2, 4 and 8 respectively. Similar performances were observed for other databases. It was further observed that the PSNR was highest when the multi-wavelet was decimated by a factor of 4.