Fingerprint Image Preprocessing and Multi-level Judg-

Individuality of fingerprints is commonly assumed by forensic experts, researchers and the general public, to the extent that the term ’fingerprint’ has become a synonym for ’something that identifies; a trait, trace, or characteristic revealing origin or responsibility’³. On the other hand, fingerprint based identification is regularly challenged in court [103, 23] pleading that the uniqueness of finger-prints is not scientifically tested and matching error rates are not established.

As a consequence, the ’Committee on Identifying the Needs of the Forensic Sci-ence Community’ of the National Research Council, USA, reports [2] the need for strengthening the scientific foundation of fingerprint based identification.

Following the assumption that a fingerprint contains a sufficient amount of discriminative information, the challenge emerges how to reliably extract this information from a fingerprint image. The second part of the thesis addresses this pattern recognition challenge in the following way:

Chapter 4 treats the task of estimating the flow of ridges resulting in an ori-entation field (OF). Special regard is paid to low quality images in which large regions are disturbed by various kinds of noise aggravating the OF estimation.

A review of relevant existing methods is given followed by the introduction of a novel OF estimation method called line sensor method which marks an im-provement in comparison to state of the art methods. In Chapter 6, curved regions are presented as an approach of dealing with naturally occurring cur-vature of ridges in fingerprints. Subsequently, the curved regions are applied for improving the ridge frequency estimation. Chapter 7 surveys widely used fingerprint image enhancement techniques and introducescurved Gabor filters.

This constitutes a synthesis of the previous two chapters, since the line sensor based OF estimation, the curved regions and the improved ridge frequency es-timation method are combined.

Chapter 8 outlines the idea of multi-level judgment aggregation as an archi-tecture for building an AFIS which makes good use of the plurality of existing methods on all levels of fingerprint image processing. Some first practical exam-ples are presented and their performance improvements is validated in verifica-tion tests [73]. The results give a hint of the potential of this approach. Known fusion techniques are examined and discussed before a novel method of score

3Merriam-Webster’s Collegiate Dictionary http://www.merriam-webster.com/

revaluation is proposed. In a case study of FVC 2002 database 2, the choice of appropriate score revaluation criteria is shown based on previously extracted in-formation, in particular the orientation field. A commercial software is applied for minutiae extraction and matching which performs on the original images at an equal error rate (EER) of 1.08 %. In Section 8.8, all previously introduced methods for line sensor based OF estimation, RF estimation using curved re-gions, image enhancement by curved Gabor filters, template cross matching and score revaluation are united under the roof of multi-level judgment aggregation leading to a perfect, i.e. error-free separation into genuine and impostor recogni-tion attempts. To the best of my knowledge, this is the first time that a perfect result was obtained on any of the FVC databases. Subsequently, the score reval-uation criteria are generalized and tested on all available FVC databases. The thesis concludes with a discussion of the achieved results and possibilities for future work within the framework of multi-level judgment aggregation.

Part I

Fingerprint Growth

Chapter 2

Fingerprint Growth

Analysis and Prediction

2.1 Introduction

The dynamics of individual and average growth in humans have been exten-sively studied [53], particularly in terms of body height. But how do fingers grow? The question of finger growth is not only of theoretical interest in biol-ogy and auxolbiol-ogy, but it has practical relevance for law enforcement agencies:

if the person being checked out had been registered as a juvenile, retrieving a matching fingerprint in their databases poses serious difficulties to existing automated fingerprint identification systems (AFIS).

Studies of human growth show strong correlations between the increase in body height and the limb lengths (see e.g. [102]). Most studies concerned with the effects of growth on fingerprints have focused on the stability of the line pat-tern’s structure. In 1892, Sir Francis Galton was among the first who noted the permanence of the configuration of individual ridges and furrows [42]. These findings were confirmed by biologists who discovered that the development of the pattern is finalized after 24 weeks estimated gestational age [7]. The de-formation of this pattern due to the fingers’ growth, however, received little attention [133]. One explanation why the effects of growth on fingerprints have not been investigated yet, may be the lack of longitudinal data of juveniles’

fingerprints through adolescence. The data sets for the growth analysis were provided by the BKA (Bundeskriminalamt, Federal Criminal Police Office of Germany). The cooperation with the BKA offered a unique opportunity to work with data which is rarely available in other respects and the systematic examination of finger pad growth contributes to strengthen the scientific founda-tion of fingerprint recognifounda-tion as requested by the Nafounda-tional Research Council [2].

The fingerprint growth analysis and prediction presented in this first part of the thesis is joint work with Thomas Hotz who especially performed the sta-tistical analysis of the data. The results can be found in a separate report [48].