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Search Space Reduction in Biometric Databases: A Review

Search Space Reduction in Biometric Databases: A Review

Ilaiah Kavati, Munaga V. N. K. Prasad, Chakravarthy Bhagvati
Copyright: © 2018 |Pages: 27
ISBN13: 9781522552048|ISBN10: 1522552049|EISBN13: 9781522552055
DOI: 10.4018/978-1-5225-5204-8.ch066
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MLA

Kavati, Ilaiah, et al. "Search Space Reduction in Biometric Databases: A Review." Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2018, pp. 1600-1626. https://doi.org/10.4018/978-1-5225-5204-8.ch066

APA

Kavati, I., Prasad, M. V., & Bhagvati, C. (2018). Search Space Reduction in Biometric Databases: A Review. In I. Management Association (Ed.), Computer Vision: Concepts, Methodologies, Tools, and Applications (pp. 1600-1626). IGI Global. https://doi.org/10.4018/978-1-5225-5204-8.ch066

Chicago

Kavati, Ilaiah, Munaga V. N. K. Prasad, and Chakravarthy Bhagvati. "Search Space Reduction in Biometric Databases: A Review." In Computer Vision: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1600-1626. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5204-8.ch066

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Abstract

Deployment of biometrics for personal recognition in various real time applications lead to large scale databases. Identification of an individual on such large biometric databases using a one-one matching (i.e., exhaustive search) increases the response time of the system. Reducing the search space during identification increases the search speed and reduces the response time of the system. This chapter presents a comprehensive review of the current developments of the search space reduction techniques in biometric databases. Search space reduction techniques for the fingerprint databases are categorized into classification and indexing approaches. For the palmprint, the current search space reduction techniques are classified as hierarchical matching, classification and indexing approaches. Likewise, the iris indexing approaches are classified as texture based and color based techniques.

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