Proceedings Article | 17 July 1998
Proc. SPIE. 3374, Signal Processing, Sensor Fusion, and Target Recognition VII
KEYWORDS: Image fusion, Statistical analysis, Databases, Image processing, Fingerprint recognition, Feature extraction, System identification, Binary data, Lawrencium, Data fusion
This paper presents a data fusion-based approach to designing an Automated Fingerprint Identification System (AFIS). Fingerprint matching methods vary from pattern matching, using ridge structure, orientation, or even the entire fingerprint itself, to point critical matching, using localized features such as ridge discontinuities, e.g. minutiae, or porous structures. Localized matching methods, such as minutiae, tend to yield more compact templates, in general, than pattern based methods. However, the reliability of localized features may be an issue, since they are affected adversely by the quality of the captured fingerprint, i.e. the degree of noise. Minutiae-based matching methods tend to be slower, albeit more accurate, than pattern-based methods. The trade-off in designing a cost-effective AFIS in terms of processing power (CPU) used, matching speed, and accuracy, lies in the choice of the proper matching methods that are selected to optimize performance by maximizing the matching accuracy while minimizing the search time. In this paper we present a systematic design and study of a fusion-based AFIS using a multiplicity of matching methods to optimize system performance and minimize required CPU cost.
