Fingerprint processing based on phase portrait model

• Main Author: Li, Jun
• Other Authors: Wang Han
• Format: Theses and Dissertations
• Published: 2008
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometrics
• Online Access: https://hdl.handle.net/10356/4645
• Institution: Nanyang Technological University

In recent decades, automatic fingerprint identification system has attracted significant interest among the scientific research community. Though large amount of effort has been made in both the academic and industrial communities, there are still unsolved problems. Noisy image processing and large scale database processing are two typical problems. Fingerprint images consist of oriented texture. Therefore the orientation information plays a very important role in fingerprint processing. Moreover, the orientation of the fingerprint follows a certain structure that is not random. Such oriented structure provides a possibility to recover the orientation information in fingerprints corrupted by noise, even for a large noise patch which cannot be solved by the traditional gradient based methods. Accurate reconstruction of the orientations is useful for fingerprint filtering, segmentation, classification and recognition. Several methods have been proposed to reconstruct the fingerprint orientation. In this thesis, phase portrait model approach is used. The first order phase portrait is demonstrated to be robust for both occluded and noisy fingerprints. It also has the capability to describe the orientation patterns of the core point and delta point in a fingerprint image. Therefore a method based on the first order phase portrait is proposed to reconstruct the local orientation near the core and delta points. Furthermore, considering the regularity of the fingerprint orientation and linearity of the first order phase portrait, a model based on the piecewise phase portrait is used to predict the orientations far away from the singular point region. The advantage of this prediction model is that it provides a way to overcome the large patch of noise or missing orientation. To obtain the fingerprint orientation with higher precision, more complicated model based on the constrained nonlinear phase portrait approach is proposed. The model employs the advantages of both the first order and high order phase portrait model. Hence accurate reconstructed orientations at both the local and global area can be obtained simultaneously. This thesis also addresses fingerprint classification using the coefficients of the constrained nonlinear phase portrait model together with the singularities information. Since the coefficients of the constrained nonlinear phase portrait model play a key role in reconstructing the fingerprint orientation, it is also expected to be able to effectively classify fingerprints. A fingerprint classification algorithm using the orientation model and the singular points' information which complement the orientation information is developed and is shown to have good classification performance.