Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Networking & Communications, Computer Security, General Computing
| Author: | Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati | ISBN: | 9783319576602 |
| Publisher: | Springer International Publishing | Publication: | May 9, 2017 |
| Imprint: | Springer | Language: | English |
| Author: | Ilaiah Kavati, Munaga V.N.K. Prasad, Chakravarthy Bhagvati |
| ISBN: | 9783319576602 |
| Publisher: | Springer International Publishing |
| Publication: | May 9, 2017 |
| Imprint: | Springer |
| Language: | English |
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.
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.
