A study of Hough Transform-based fingerprint alignment algorithms

Abstract

This paper classifies existing Hough Transform fingerprint alignment algorithms and compares their performance to determine the one that gives optimal alignment results (translation and rotation). The classification is performed by considering the implementation of each algorithm. The comparison is performed by considering the alignment results computed using each group of algorithms when varying number of minutiae points, rotation angle, and translation. In addition, the memory usage, computing time and accuracy are taken into consideration. The experiments were performed on a small database where fingerprints were captured in different orientations and locations and on the public database FVC2004. Three classes of Hough Transform-Based approaches were classified as the Local Match Based Alignment(LMBA), Discretized Rotation Based Alignment(DRBA) and Matching Pair Based Alignment (MPBA). The results revealed good accuracy on all three approaches, however, the computing time and memory usage affected the performance of each approach. The LMBA approach perform better than the DRBA and the MPBA approaches on minutiae points set with larger rotation and small number of points. The DRBA approach was found to perform better with minutiae points with large amount of translation, and the computational time was less than that of LMBA and the MPBA approaches. However, the memory usage required in DRBA and MPBA for the accumulator array is greater than memory required in LMBA.