Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale SystemsEfficient Score-Based Indexing Technique for Fast Palmprint Retrieval

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale Systems: Efficient... [Biometric identification systems capture biometric (i.e., fingerprint, palm, and iris) images and store them in a central database. During identification, the query biometric image is compared against all images in the central database. Typically, this exhaustive matching process (linear search) works very well for the small databases. However, biometric databases are usually huge and this process increases the response time of the identification system. To address this problem, we present an efficient technique that computes a fixed-length index code for each biometric image. Further, an index table is created based on the indices of all individuals. During identification, a set of candidate images which are similar to the query are retrieved from the index table based on the values of query index using voting scheme that takes less time. The technique has been tested on benchmark PolyU palmprint database and the results show a better performance in terms of response time and search speed compared to the state-of-the-art indexing methods.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Efficient Biometric Indexing and Retrieval Techniques for Large-Scale SystemsEfficient Score-Based Indexing Technique for Fast Palmprint Retrieval

Download PDF
10 pages

Loading next page...
 
/lp/springer-journals/efficient-biometric-indexing-and-retrieval-techniques-for-large-scale-9zbO8ZPZ6s
Publisher
Springer International Publishing
Copyright
© The Author(s) 2017
ISBN
978-3-319-57659-6
Pages
41 –51
DOI
10.1007/978-3-319-57660-2_3
Publisher site
See Chapter on Publisher Site

Abstract

[Biometric identification systems capture biometric (i.e., fingerprint, palm, and iris) images and store them in a central database. During identification, the query biometric image is compared against all images in the central database. Typically, this exhaustive matching process (linear search) works very well for the small databases. However, biometric databases are usually huge and this process increases the response time of the identification system. To address this problem, we present an efficient technique that computes a fixed-length index code for each biometric image. Further, an index table is created based on the indices of all individuals. During identification, a set of candidate images which are similar to the query are retrieved from the index table based on the values of query index using voting scheme that takes less time. The technique has been tested on benchmark PolyU palmprint database and the results show a better performance in terms of response time and search speed compared to the state-of-the-art indexing methods.]

Published: May 10, 2017

Keywords: Index code; Palmprint; SIFT; Sample images; Match scores

There are no references for this article.