Abstract | ||
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•An efficient framework for extracting consistent bits from IrisCodes is proposed.•A criteria (t-consistency) is defined for quantifying the stability of IrisCodes.•Use of information from iris masks mitigates effects of noise on extracted bits.•Extensive empirical results have been validated on two benchmark iris databases. |
Year | DOI | Venue |
---|---|---|
2020 | 10.1016/j.eswa.2019.112884 | Expert Systems with Applications |
Keywords | Field | DocType |
Biometrics,IrisCode,Consistency,Clustering | Data mining,Cluster (physics),Pattern recognition,Computer science,Invariant (mathematics),Artificial intelligence,Biometrics,Binary number | Journal |
Volume | ISSN | Citations |
140 | 0957-4174 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Debanjan Sadhya | 1 | 15 | 3.63 |
Kanjar De | 2 | 0 | 0.34 |
Balasubramanian Raman | 3 | 679 | 70.23 |
Partha Pratim Roy | 4 | 597 | 77.02 |