Paper Info
Title
Iris Data Indexing Method Using Gabor Energy Features
Abstract
Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3%, 14.5%, 16.3%, 13.5%, and 10.3% penetration rates and 98.2%, 91.1%, 90.7%, 85.2%, and 96% hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases.
Year
DOI
Venue
2012
10.1109/TIFS.2012.2196515
IEEE Transactions on Information Forensics and Security
Keywords
Field
DocType
Gabor filters,feature extraction,image retrieval,image texture,iris recognition,Bath iris database,CASIA-V3-Interval iris database,CASIA-V4-Thousand iris database,Gabor energy feature,MMU2 iris database,WVU iris database,acceptance rate,biometric data retrieval,biometric feature extraction,high dimensional data,identification process,image retrieval,iris biometric template retrieval,iris data indexing method,iris template retrieval,iris texture preprocessing,linear search method,query index key,query template matching,Gabor energy calculation,image retrieval,indexing biometric data,iris biometric,personal identification
Hit rate,Iris recognition,Computer vision,Pattern recognition,Image texture,Computer science,Image retrieval,Search engine indexing,Feature extraction,Artificial intelligence,Iris flower data set,Biometrics
Journal
Volume
Issue
ISSN
7
4
1556-6013
Citations 
PageRank 
References 
14
0.62
23
Authors
2
Name
Order
Citations
PageRank
Somnath Dey1140.96
Debasis Samanta2150.98