Downloads provided by UsageCounts
Fingerprints are considered as a unique identification of a person and due to easy access its the best and one of the fastest method used in biometric identification systems. They are unique, so secure and reliable to use and doesnt change for one in a lifetime. And beside these things fingerprint recognition specially using minutiae matching technique is cheap, reliable and accurate up to a satisfactory limits. In this thesis work, we propose a method for fingerprint matching based on minutiae matching. However, unlike conventional minutiae matching algorithms our algorithm also takes into account region and line structures that exist between minutiae pairs. This allows for more structural information of the fingerprint to be accounted for thus resulting in stronger certainty of matching minutiae. Also, since most of the region analysis is preprocessed it does not make the algorithm slower.
Fingerprint Pattern Matching, Minutiae, FRR, FAR, ROC, EER.
Fingerprint Pattern Matching, Minutiae, FRR, FAR, ROC, EER.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 2 | |
| downloads | 3 |

Views provided by UsageCounts
Downloads provided by UsageCounts