PRECISE FINGERPRINT ENROLMENT THROUGH PROJECTION INCORPORATED SUBSPACE BASED ON PRINCIPAL COMPONENT ANALYSIS (PCA)

Abstract

Despite recent advances in the area of fingerprint identification, fingerprint enrolment continues to be a challenging pattern recognition problem. The first step to this problem is the enhancement of landmarks as well as precise minutiae points (ridge bifurcation and ridge ending), core, plain ridges from a print. Once enhanced, these fingerprint images are then ready to extract features and store into a database. Later these are compared to all sets on file in search of a match. The accurate fingerprint image is the basis for the entire identification and matching process. Various enhancement approaches have been proposed in the literature, each with its own merits and degree of success. The most common approach is to enhance and store the precise fingerprint image through normalization, orientation, frequencies calculation, contextual filtering and then binarisation and masking. Our emphasis in this paper is to enhance and store the fingerprint image accurately using Projection Incorporated Subspace based on Principal Component Analysis (PCA). In particular, we have implemented the methods based on eigenspace representations and neural network classifiers. Moreover, we present preliminary results of an attempt to mingle the outputs of these methods using a clustering algorithm unique to this type of problem.