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.
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.
