Improved fingerprint identification with supervised filtering enhancement

Abstract

An important step in the fingerprint identification system is the reliable extraction of distinct features from fingerprint images. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial-intelligence-based feature-extraction techniques are attractive owing to their adaptive learning properties. We present a new supervised filtering technique that is based on a dynamic neural-network approach to develop a robust fingerprint enhancement algorithm. For pattern matching, a joint transform correlation (JTC) algorithm has been incorporated that offers high processing speed for real-time applications. Because the fringe-adjusted JTC algorithm has been found to yield a significantly better correlation output compared with alternate JTCs, we used this algorithm for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.

Binary Nonlinear Joint Transform Correlator with Sinusoidal Iterative Filter in Spectrum Domain

“…All of the obtained values of the ratio shown on Table 2 are for the match case, representatively. The left side of Table 2 is for the case of using Alam's iterative filter processing [13], the middle of Table 2 is for the case of using our previously presented iterative filter processing [14], and finally the right side of Table 2 is for the case of our new cosine amplitude modulated iterative filter processing. First, for the case of using Alam's iterative filter in the spatial domain, concerning the correlation peaks, the iterative filter process yields enhanced results that are a maximum of 109.00 times better than those for the case without the iterative filter processing.…”

“…Unlike technique, our technique has dealt with fringe-adjustment by inserting an iterative filtering step in the joint power spectrum (JPS) domain to enhance the interference fringe pattern instead of inserting the iterative filter after the input joint images. Although this technique successfully increased the PSNR ratio to reduce the side-lobe noise, it showed rather low correlation peak ratio compared with Alam's technique [14]. In order to enhance both the correlation peak and the PSNR, we present a new technique of modifications of the interference fringes by introducing a sinusoidal amplitudemodulated iterative filter convolved with the interference fringe in the JPS domain.…”

“…However, most attempts are divided into two groups, one group uses an intensity or a phase threshold and the other group uses some kinds of filtering. Alam successfully applied the synthetic discrimination function (SDF) filter concept to a JTC system and used a Fourier plane image subtraction technique [12][13][14]. Although this technique successfully improved the correlation peak and reduced the side-lobe noise, it adopts a simple input joint images enhancement using an image normalization and spatial domain iterative filtering process.…”

Color Pattern Recognition with Recombined Single Input Channel Joint Transform Correlator

“…The joint transform correlator (JTC) has shown remarkable achievements for real time pattern recognition and target tracking applications [8][9][10][11][12][13][14][15][16]. The introduction of color information in the pattern recognition has become increasingly important, especially because of the wide spread of electronic image-acquisition devices such as color CCD cameras.…”

New Iterative Filter for Fringe Adjustment of Joint Transform Correlator

“…However, most attempts are divided into two groups, one group uses an intensity or a phase threshold and the other group uses some kinds of filtering. Karim successfully applied the synthetic discrimination function (SDF) filter concept to a JTC system and use a Fourier plane image subtraction technique [13][14][15]. Although this technique successfully improved the correlation peak and reduced the side-lobe noise, it adopts a simple image normalization and filtering process or spatial domain iterative filtering process.…”