As information technologies advanced greatly in recent years, the security problem of information networks becomes all the more important. As a result, biometric identification techniques have been given considerable attention. Fingerprint-related techniques, due to these desirable properties, i.e., universality, perpetuity, collectability and particularity, are most widely applied and documented. However, in practice, collected fingerprint images are not always of good quality. They often are noisy or are even partial images. Therefore, in this research, we propose a new minutiae matching method, while using a coefficient of variation of orientation difference, a coefficient of frequency correlation, along with other image features to obtain a match based on only partial fingerprints. By the proposed method, when a score is arrived at and the test image and the database image have five minutia points matched, we have both FRR and FAR values close to 29%, and the correctness of matching reaches 70.56%.

This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fin gerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale.