Automated Fingerprint Identification System (AFIS)
How does AFIS operate?
Effective use of highly sophisticated algorithms is a crucial element of the process. Over the years, many such algorithms have been developed, and enhanced continuously on the basis of real world experience. Commonly used examples include:
- Image Enhancement
As the name suggests, image enhancement algorithms address the numerous issues that can affect the basic quality of latent or tenprint images.
- Feature Extraction
Feature extraction algorithms are designed to identify the minutiae points (usually ridge endings and ridge bifurcations) that distinguish one print from another. These might also be supported by algorithms that can identify non-minutiae points, such as pores or textures. Indeed the combination of both minutiae and non-minutiae algorithms can prove particularly powerful in the search for a match.
Automatic indexing of fingerprints limits the sheer volume of data that an AFIS needs to process when searching for a match, significantly reducing the time taken to complete the task.
The design and choice of matching algorithms employed by the AFIS – and its operators – has a major impact on the number of potential matches, false positives and false negatives generated. Algorithms are also employed by an AFIS to provide a ‘matching score’. This reflects the confidence with which a set of prints can be regarded as matching another found in the database.