ECH-A NOVEL MULTILEVEL THRESHOLDING TECHNIQUE FOR MINUTIAE BASED FINGERPRINT RECOGNITION

Abstract

Thresholding is probably the most frequently used technique in segmenting an image. Several computationally efficient thresholding algorithms have been shown to offer threshold value, however whilst many of the methods will give similar guarantees. In this proposed research work, a novel multilevel global thresholding algorithm ECH (pronounced as eh-chch) is introduced. The algorithm uses HAT transformation to maintain uniform intensity in the gray scale images. The optimum threshold value is decided by entropying of the background and foreground regions, where the gray scale samples are clustered as a mixture of two Gaussians for background and foreground objects, using which the peaks and valleys like minutiae points are analyzed. This paper also explores a range of steps implicated in FP recognition. The performances of the algorithm are compared with NEST DB4, DB2 FVC2002, DB2 FVC2004 databases. The performance indices FAR (False Acceptance Ratio) less than 1.3% and FRR (False Rejection Ratio) almost 0% is reported using MATLAB. The experimental results on MATLAB shows prominent result on gray scale finger print images.