Name: Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems (en Inglés)
SKU: 58790911
Price: 59.29 USD
Availability: InStock
Author: Kavati, Ilaiah ; Prasad, Munaga V. N. K. ; Bhagvati, Chakravarthy
ISBN: 9783319576596

portada Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems (en Inglés)

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems (en Inglés)

Formato

Libro Físico

Autor

Munaga V. N. K. Prasad

Ilaiah Kavati

Chakravarthy Bhagvati

Editorial

Springer

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

23.4x15.6x0.5 cm

Peso

0.14 kg.

ISBN13

9783319576596

Categorías

Medios de almacenamiento y dispositivos periféricos
Recuperación de información
Seguridad informática
Administración del sistema
Reconocimiento de formas
Visión por ordenador

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems (en Inglés)

Munaga V. N. K. Prasad (Autor) · Ilaiah Kavati (Autor) · Chakravarthy Bhagvati (Autor) · Springer · Tapa Blanda

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Reseña del libro "Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems (en Inglés)"

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.