Improvement of Biometric Authentication System Applying Fingerprint
Abstract
The biometric system plays an important role in everyone life. To identify one identity, the finger is one of many forms of the biometrics are generally used. The fingerprint is the verified function to identify a match between two person’s fingerprints. Here a simple and effective system for biometric fingerprint based voter identity system has been proposed that is based on image enhancement and correct minutiae extraction. Automatic and reliable extraction of minutiae from fingerprint images is a critical step in fingerprint matching. In this research a fast fingerprint enhancement and minutiae extraction algorithm have been presented which improve the clarity of the ridge and valley structures of the input fingerprint images based on the frequency and orientation of the local ridges and thereby extracting correct minutiae.
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Introduction
A biometric system gives automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. Biometric systems have been created based on fingerprints, facial features, voice, hand geometry, handwriting, the retina [1], and the systems work by first capturing a sample of the feature, such as recording a digital sound of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates.
A fingerprint is the pattern of curved lines on the end of a finger or thumb that is distinctive in every person or a mark left by this pattern. However, shown by intensive research on fingerprint recognition, fingerprints are not distinguished by their ridges and furrows, but by Minutia, which are some abnormal points on the ridges.
Conclusion
This research has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into the research paper. Also, some novel changes like segmentation using Morphological operations, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in my replace, which are not reported in other literature I referred to. Also, a program coding with MATLAB going through all the stages of the fingerprint recognition is built. It is helpful to understand the procedures of fingerprint recognition and demonstrate the key issues of fingerprint recognition.