Implementing Fusion Technique Using Biorthogonal Dwt to Increase the Number of Minutiae in Fingerprint Images

Abstract

Biometric devices identify persons based on the minutiae extracted from fingerprint images. Image quality is very important in this process. Usually, fingerprint images have low quality and in many cases they are obtained in various positions. The paper focuses on increasing minutiae detected number by fusing two fingerprint images obtained in various positions. Biorthogonal wavelets have advantages compared to orthogonal wavelets. Fusion is performed in wavelet domain by implementing biorthogonal wavelet. Terminations and bifurcations are extracted from the original and fused images using licensed software Papillon 9.02 and manually extraction by an expert. Biorthogonal Wavelet transform is implemented in the image fusion process, yielding in the increased number of the minutiae compared to the original one. Different biorthogonal wavelets are experimented and various results are obtained. Finding the appropriate wavelet is important in the fusion process since it has a direct impact in the number of minutiae extracted. Based on the number of minutiae and MSE results, the appropriate wavelet to be used in the fusion process is defined.

John von Neumann’s Space-Frequency Orthogonal Transforms

“…The JvN coefficients (25) can be grouped into a 4D array and cannot be represented visually. To compute them, the previously mentioned symmetry property can be exploited for each harmonic index, provided that the 2D signal is real-valued.…”

“…Since their inception by Meyer, Mallat and Daubechies (more than 35 years ago), the literature reporting how these wavelets work in conjunction with images has become so vast that any attempt to encompass all the sound (and sometimes amazing) results is very likely doomed to fail. One can only cite a few interesting recent works, such as [23] (with application to image filtering), [24] (in which principal component analysis is developed by means of wavelets, aiming to achieve image denoising), [25] (where image fusion is performed) and [26] (which deals with the efficient implementation of 2D wavelet transforms).…”