Minutiae Triangle Graphs: A New Fingerprint Representation with Invariance Properties

A new algorithm for matching finger or palm prints is presented for use where the full hand is considered as a biometric and only parts may be available in images for comparison. The algorithm uses an extended version of the minutiae-based approach treating the pattern as a graph of minutiae-like points. The procedure to identify minutiae-like points uses Gabor filtering, edge detection and thinning and following line patterns. A set of such points is subjected to Delaunay triangulation yielding a starting set of base-triangles for matching. There can be multiple matches of such triangles between the template and test - as similar triangles with a tolerance in the angles. Graphs are then grown to 5 and more nodes as long as a match can be found, until the maximum size matching graph is obtained. If the test matches a significant part of the template, the maximum order of graph matched will be high. The matching process is robust to transformations such as rotation, translation and scale changes. It can be applied to any part of the hand provided minutiae-like points are identifiable prior to the matching steps. The algorithm is tested using 158 fingerprint images from FVC 2002 DB1. 100 genuine and 5048 impostor scores are generated from 46 templates and 112 testing images. It had an EER of about 6%. It proves the principle behind the methodology and demonstrates that the method can be effective with degraded fingerprint images and is robust to similarity transformations present in the data. It can be applied for forensic fingerprint matching from the palm or parts other than the fingertips. By using multiple parts and multiple templates, the accuracy of the method will be improved with fusion in future versions of the algorithm.

I. Introduction

Fingerprints have been an attractive biometric modality, especially for forensic applications for over 100 years. Automated fingerprint identification and retrieval work well when the acquisition systems during enrollment and test are matched and the images are of good quality. However, for forensics and law enforcement, latent prints (photographic images transferred from crime scenes) have to be matched with entities in a database of fingerprints acquired using other sensors and this is still a difficult proposition. Latent prints are often of poor quality and degraded in parts. They do not get through preliminary operations successfully or well enough for automated minutiae matching to perform well. High-resolution cameras make it possible to obtain detailed images of palms and fingers and they have been investigated as potential biometric modalities.