A study on fingerprint image enhancement and minutiae extraction techniques

Abstract

Existing security measures rely on knowledge-based approaches like passwords or token based approaches such as swipe cards and passports to control access to physical and virtual spaces. Though ubiquitous, such methods are not very secure. Tokens such as badges and access cards may be shared or stolen. Furthermore, they cannot differentiate between authorized user and a person having access to the tokens or passwords. Biometrics such as fingerprint, face and voice print offers means of reliable personal authentication that can address these problems and is gaining citizen and government acceptance. Fingerprints were one of the first forms of biometric authentication to be used for law enforcement and civilian applications. Reliable extraction of features from poor quality prints is the most challenging problem faced in the area of fingerprint recognition. In this thesis, we introduce a new approach for fingerprint image enhancement based on the Gabor filter have been widely used to facilitate various fingerprint applications such as fingerprint matching and fingerprint classification. Gabor filters are band pass filters that have both frequency- selective and orientation-selective properties, which means the filters can be effectively tuned to specific frequency and orientation values. The proposed analysis and enhancement algorithm simultaneously estimates several intrinsic properties of the fingerprint such as the foreground region mask, local ridge orientation and local frequency. We also objectively measure the effectiveness of the enhancement algorithm and show that it can improve the sensitivity and recognition accuracy of existing feature extraction and matching algorithms. We also present a new feature extraction algorithm is the Crossing Number (CN) concept. This method involves the use of the skeleton image where the ridge flow pattern is eightconnected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3x3 window. The CN value is then computed, which is defined as half the sum of the differences between pairs of adjacent pixels in the eight-neighborhood. The algorithm has several advantages over the techniques proposed in literature such as increased computational efficiency, improved localization and higher sensitivity.