| 书目名称 | Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems |
| 编辑 | Ilaiah Kavati,Munaga V.N.K. Prasad,Chakravarthy Bh |
| 视频video | |
| 概述 | Presents an efficient indexing approach using minutiae triplets for biometric databases.Describes a score-based indexing technique that demonstrates a decreased retrieval time and enhanced identificat |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | This work presents a review of different indexing techniques designed to enhance the speed and efficiency of searches over large biometric databases. The coverage includes an extended Delaunay triangulation-based approach for fingerprint biometrics, involving a classification based on the type of minutiae at the vertices of each triangle. This classification is demonstrated to provide improved partitioning of the database, leading to a significant decrease in the number of potential matches during identification. This discussion is then followed by a description of a second indexing technique, which sorts biometric images based on match scores calculated against a set of pre-selected sample images, resulting in a rapid search regardless of the size of the database. The text also examines a novel clustering-based approach to indexing with decision-level fusion, using an adaptive clustering algorithm to compute a set of clusters represented by a ‘leader’ image, and then determining the index code from the set of leaders. This is shown to improve identification performance while using minimal resources.. |
| 出版日期 | Book 2017 |
| 关键词 | Biometrics; Information retrieval; Large-scale systems; Search indexing; Pattern recognition |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-57660-2 |
| isbn_softcover | 978-3-319-57659-6 |
| isbn_ebook | 978-3-319-57660-2Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2017 |
发表于 2025-3-21 19:57:41
