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Fingerprint direct-access strategy using local-star-structure-based discriminator features: A comparison study
Indrawan G.a, Akbar S.a, Sitohang B.a
a Data and Software Engineering Research Division, School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40243, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Copyright © 2014 Institute of Advanced Engineering and Science. All rights reserved.This paper describes a comparison study of the proposed fingerprint direct-access strategy using local-star-topology-based discriminator features, including internal comparison among different concerned configurations, and external comparison to the other strategies. Through careful minutiae-based feature extraction, hashing-based indexing-retrieval mechanism, variable-threshold-on-score-ratio-based candidate-list reduction technique, and hill-climbing learning process, thisstrategy was considered promising, as confirmed by the experiment results. For particular aspect of external accuracy comparison, thiss trategy outperformed the others over three publicdata sets, i.e. up to Penetration Rate (PR) 5%, it consistently gave lower Error Rate (ER). By taking sample at PR 5%, this strategy produced ER 4%, 10%, and 1% on FVC2000 DB2A, FVC2000 DB3A, and FVC2002 DB1A, respectively. Another perspective if accuracy performance was based on area under curve of graph ER and PR, this strategy neither is the best nor the worst strategy on FVC2000 DB2A and FVC2000 DB3A, while on FVC2002 DB1A it outperfomed the others and even it gave impressive results for index created by three impressions per finger (with or without NT) by ideal step down curve where PR equal to 1% can always be maintained for smaller ER.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Direct-access,ErrorRate,Fingerprint,Local-star,Penetration rate[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.11591/ijece.v4i5.6589[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]