In recent years, biometric applications have significantly gained popularity. Such applications involve voluminous databases of high dimensional data. These enormous databases increase the cost of identification and degrade the system performance. To resolve such an issue a plethora of algorithms based on geometric hashing, k–d tree, k-means clustering, etc., have been proposed in the literature. Although, these algorithms solve a number of concomitant challenges of multi-dimensional data, yet, they fail to present a universal solution.
In this study, we propose an indexing mechanism, which partitions the data space effectively into zones and blocks using a set of hash functions. Furthermore, the index locations are divided into maximum nine sub-locations to store data. This helps in carrying out an efficient search of the queried data, thereby minimising the false acceptance and rejection rate.
To validate the proposed approach, the mechanism has been applied to the fingerprint verification competition and National Institute of Standards and Technology fingerprint image databases. The experimental results substantiate the efficacy of our approach in terms of accuracy, speed, reduction of search space and the number of comparisons to store and retrieve data.
• Indexing approach is to store fingerprint data independently and then perform a linear search, thereby matching a query fingerprint against a database of N enrolled fingerprints . Hence, the approach is efficient only for very small data sets.
• Henry classification is the earliest work, which divides a large fingerprint database into groups based on fingerprint patterns. Though, this classification significantly helps in many-to-one matching, but, the approach is futile for identification task with high-dimensional data.
In Our project, we have proposed a novel approach to data indexing which is independent of the size of the database and dimensionality of data. Furthermore, the proposed approach is fast and accurate. In this work, we have tested our approach to the biometric data namely fingerprint. More significantly the approach can be applied to any data in many applications.
• It is independent of the size of the database.
• The search space required to find the best match is reduced remarkably.
• The indexing approach is independent of the dimensionality of data.
Processor Type : Pentium -IV
Speed : 2.4 GHZ
Ram : 128 MB RAM
Hard disk : 20 GB HD
Operating System : Windows 7
Software Programming Package : Matlab R2014a
Mordini, E., Tzovaras, D.: ‘Second generation biometrics: the ethical, legal and social context’ (Springer, Dordrecht, 2012)