Fingerprint classification by a hierarchical classifier
Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical cla...
Ausführliche Beschreibung
Autor*in: |
Cao, Kai [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2013transfer abstract |
---|
Schlagwörter: |
---|
Umfang: |
12 |
---|
Übergeordnetes Werk: |
Enthalten in: Association between dopa decarboxylase gene variants and borderline personality disorder - Mobascher, Arian ELSEVIER, 2014, the journal of the Pattern Recognition Society, Amsterdam |
---|---|
Übergeordnetes Werk: |
volume:46 ; year:2013 ; number:12 ; pages:3186-3197 ; extent:12 |
Links: |
---|
DOI / URN: |
10.1016/j.patcog.2013.05.008 |
---|
Katalog-ID: |
ELV027691217 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | ELV027691217 | ||
003 | DE-627 | ||
005 | 20230625152308.0 | ||
007 | cr uuu---uuuuu | ||
008 | 180603s2013 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1016/j.patcog.2013.05.008 |2 doi | |
028 | 5 | 2 | |a GBVA2013022000026.pica |
035 | |a (DE-627)ELV027691217 | ||
035 | |a (ELSEVIER)S0031-3203(13)00212-4 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | |a 000 |a 150 | |
082 | 0 | 4 | |a 000 |q DE-600 |
082 | 0 | 4 | |a 150 |q DE-600 |
100 | 1 | |a Cao, Kai |e verfasserin |4 aut | |
245 | 1 | 0 | |a Fingerprint classification by a hierarchical classifier |
264 | 1 | |c 2013transfer abstract | |
300 | |a 12 | ||
336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
337 | |a nicht spezifiziert |b z |2 rdamedia | ||
338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
520 | |a Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. | ||
520 | |a Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. | ||
650 | 7 | |a Ridge line flow |2 Elsevier | |
650 | 7 | |a Hierarchical classifier |2 Elsevier | |
650 | 7 | |a Complex filter response |2 Elsevier | |
650 | 7 | |a Support vector machine |2 Elsevier | |
650 | 7 | |a Fingerprint classification |2 Elsevier | |
700 | 1 | |a Pang, Liaojun |4 oth | |
700 | 1 | |a Liang, Jimin |4 oth | |
700 | 1 | |a Tian, Jie |4 oth | |
773 | 0 | 8 | |i Enthalten in |n Elsevier |a Mobascher, Arian ELSEVIER |t Association between dopa decarboxylase gene variants and borderline personality disorder |d 2014 |d the journal of the Pattern Recognition Society |g Amsterdam |w (DE-627)ELV017326583 |
773 | 1 | 8 | |g volume:46 |g year:2013 |g number:12 |g pages:3186-3197 |g extent:12 |
856 | 4 | 0 | |u https://doi.org/10.1016/j.patcog.2013.05.008 |3 Volltext |
912 | |a GBV_USEFLAG_U | ||
912 | |a GBV_ELV | ||
912 | |a SYSFLAG_U | ||
951 | |a AR | ||
952 | |d 46 |j 2013 |e 12 |h 3186-3197 |g 12 | ||
953 | |2 045F |a 000 |
author_variant |
k c kc |
---|---|
matchkey_str |
caokaipangliaojunliangjimintianjie:2013----:igrrncasfctobairrh |
hierarchy_sort_str |
2013transfer abstract |
publishDate |
2013 |
allfields |
10.1016/j.patcog.2013.05.008 doi GBVA2013022000026.pica (DE-627)ELV027691217 (ELSEVIER)S0031-3203(13)00212-4 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cao, Kai verfasserin aut Fingerprint classification by a hierarchical classifier 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Elsevier Pang, Liaojun oth Liang, Jimin oth Tian, Jie oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:46 year:2013 number:12 pages:3186-3197 extent:12 https://doi.org/10.1016/j.patcog.2013.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 46 2013 12 3186-3197 12 045F 000 |
spelling |
10.1016/j.patcog.2013.05.008 doi GBVA2013022000026.pica (DE-627)ELV027691217 (ELSEVIER)S0031-3203(13)00212-4 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cao, Kai verfasserin aut Fingerprint classification by a hierarchical classifier 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Elsevier Pang, Liaojun oth Liang, Jimin oth Tian, Jie oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:46 year:2013 number:12 pages:3186-3197 extent:12 https://doi.org/10.1016/j.patcog.2013.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 46 2013 12 3186-3197 12 045F 000 |
allfields_unstemmed |
10.1016/j.patcog.2013.05.008 doi GBVA2013022000026.pica (DE-627)ELV027691217 (ELSEVIER)S0031-3203(13)00212-4 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cao, Kai verfasserin aut Fingerprint classification by a hierarchical classifier 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Elsevier Pang, Liaojun oth Liang, Jimin oth Tian, Jie oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:46 year:2013 number:12 pages:3186-3197 extent:12 https://doi.org/10.1016/j.patcog.2013.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 46 2013 12 3186-3197 12 045F 000 |
allfieldsGer |
10.1016/j.patcog.2013.05.008 doi GBVA2013022000026.pica (DE-627)ELV027691217 (ELSEVIER)S0031-3203(13)00212-4 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cao, Kai verfasserin aut Fingerprint classification by a hierarchical classifier 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Elsevier Pang, Liaojun oth Liang, Jimin oth Tian, Jie oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:46 year:2013 number:12 pages:3186-3197 extent:12 https://doi.org/10.1016/j.patcog.2013.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 46 2013 12 3186-3197 12 045F 000 |
allfieldsSound |
10.1016/j.patcog.2013.05.008 doi GBVA2013022000026.pica (DE-627)ELV027691217 (ELSEVIER)S0031-3203(13)00212-4 DE-627 ger DE-627 rakwb eng 000 150 000 DE-600 150 DE-600 Cao, Kai verfasserin aut Fingerprint classification by a hierarchical classifier 2013transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Elsevier Pang, Liaojun oth Liang, Jimin oth Tian, Jie oth Enthalten in Elsevier Mobascher, Arian ELSEVIER Association between dopa decarboxylase gene variants and borderline personality disorder 2014 the journal of the Pattern Recognition Society Amsterdam (DE-627)ELV017326583 volume:46 year:2013 number:12 pages:3186-3197 extent:12 https://doi.org/10.1016/j.patcog.2013.05.008 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U AR 46 2013 12 3186-3197 12 045F 000 |
language |
English |
source |
Enthalten in Association between dopa decarboxylase gene variants and borderline personality disorder Amsterdam volume:46 year:2013 number:12 pages:3186-3197 extent:12 |
sourceStr |
Enthalten in Association between dopa decarboxylase gene variants and borderline personality disorder Amsterdam volume:46 year:2013 number:12 pages:3186-3197 extent:12 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Ridge line flow Hierarchical classifier Complex filter response Support vector machine Fingerprint classification |
dewey-raw |
000 |
isfreeaccess_bool |
false |
container_title |
Association between dopa decarboxylase gene variants and borderline personality disorder |
authorswithroles_txt_mv |
Cao, Kai @@aut@@ Pang, Liaojun @@oth@@ Liang, Jimin @@oth@@ Tian, Jie @@oth@@ |
publishDateDaySort_date |
2013-01-01T00:00:00Z |
hierarchy_top_id |
ELV017326583 |
dewey-sort |
0 |
id |
ELV027691217 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV027691217</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625152308.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.patcog.2013.05.008</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2013022000026.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV027691217</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0031-3203(13)00212-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">000</subfield><subfield code="a">150</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cao, Kai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fingerprint classification by a hierarchical classifier</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ridge line flow</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hierarchical classifier</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Complex filter response</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Support vector machine</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fingerprint classification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pang, Liaojun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Jimin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Jie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Mobascher, Arian ELSEVIER</subfield><subfield code="t">Association between dopa decarboxylase gene variants and borderline personality disorder</subfield><subfield code="d">2014</subfield><subfield code="d">the journal of the Pattern Recognition Society</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV017326583</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:46</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:12</subfield><subfield code="g">pages:3186-3197</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.patcog.2013.05.008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">46</subfield><subfield code="j">2013</subfield><subfield code="e">12</subfield><subfield code="h">3186-3197</subfield><subfield code="g">12</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">000</subfield></datafield></record></collection>
|
author |
Cao, Kai |
spellingShingle |
Cao, Kai ddc 000 ddc 150 Elsevier Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Fingerprint classification by a hierarchical classifier |
authorStr |
Cao, Kai |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV017326583 |
format |
electronic Article |
dewey-ones |
000 - Computer science, information & general works 150 - Psychology |
delete_txt_mv |
keep |
author_role |
aut |
collection |
elsevier |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
000 150 000 DE-600 150 DE-600 Fingerprint classification by a hierarchical classifier Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification Elsevier |
topic |
ddc 000 ddc 150 Elsevier Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification |
topic_unstemmed |
ddc 000 ddc 150 Elsevier Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification |
topic_browse |
ddc 000 ddc 150 Elsevier Ridge line flow Elsevier Hierarchical classifier Elsevier Complex filter response Elsevier Support vector machine Elsevier Fingerprint classification |
format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
zu |
author2_variant |
l p lp j l jl j t jt |
hierarchy_parent_title |
Association between dopa decarboxylase gene variants and borderline personality disorder |
hierarchy_parent_id |
ELV017326583 |
dewey-tens |
000 - Computer science, knowledge & systems 150 - Psychology |
hierarchy_top_title |
Association between dopa decarboxylase gene variants and borderline personality disorder |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)ELV017326583 |
title |
Fingerprint classification by a hierarchical classifier |
ctrlnum |
(DE-627)ELV027691217 (ELSEVIER)S0031-3203(13)00212-4 |
title_full |
Fingerprint classification by a hierarchical classifier |
author_sort |
Cao, Kai |
journal |
Association between dopa decarboxylase gene variants and borderline personality disorder |
journalStr |
Association between dopa decarboxylase gene variants and borderline personality disorder |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works 100 - Philosophy & psychology |
recordtype |
marc |
publishDateSort |
2013 |
contenttype_str_mv |
zzz |
container_start_page |
3186 |
author_browse |
Cao, Kai |
container_volume |
46 |
physical |
12 |
class |
000 150 000 DE-600 150 DE-600 |
format_se |
Elektronische Aufsätze |
author-letter |
Cao, Kai |
doi_str_mv |
10.1016/j.patcog.2013.05.008 |
dewey-full |
000 150 |
title_sort |
fingerprint classification by a hierarchical classifier |
title_auth |
Fingerprint classification by a hierarchical classifier |
abstract |
Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. |
abstractGer |
Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. |
abstract_unstemmed |
Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection. |
collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U |
container_issue |
12 |
title_short |
Fingerprint classification by a hierarchical classifier |
url |
https://doi.org/10.1016/j.patcog.2013.05.008 |
remote_bool |
true |
author2 |
Pang, Liaojun Liang, Jimin Tian, Jie |
author2Str |
Pang, Liaojun Liang, Jimin Tian, Jie |
ppnlink |
ELV017326583 |
mediatype_str_mv |
z |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth oth oth |
doi_str |
10.1016/j.patcog.2013.05.008 |
up_date |
2024-07-06T16:53:18.408Z |
_version_ |
1803849352778612736 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV027691217</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625152308.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2013 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.patcog.2013.05.008</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2013022000026.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV027691217</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0031-3203(13)00212-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">000</subfield><subfield code="a">150</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Cao, Kai</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fingerprint classification by a hierarchical classifier</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Fingerprint classification is still a challenging problem due to large intra-class variability, small inter-class variability and the presence of noise. To deal with these difficulties, we propose a regularized orientation diffusion model for fingerprint orientation extraction and a hierarchical classifier for fingerprint classification in this paper. The proposed classification algorithm is composed of five cascading stages. The first stage rapidly distinguishes a majority of Arch by using complex filter responses. The second stage distinguishes a majority of Whorl by using core points and ridge line flow classifier. In the third stage, K-NN classifier finds the top two categories by using orientation field and complex filter responses. In the fourth stage, ridge line flow classifier is used to distinguish Loop from other classes except Whorl. SVM is adopted to make the final classification in the last stage. The regularized orientation diffusion model has been evaluated on a web-based automated evaluation system FVC-onGoing, and a promising result is obtained. The classification method has been evaluated on the NIST SD 4. It achieved a classification accuracy of 95.9% for five-class classification and 97.2% for four-class classification without rejection.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Ridge line flow</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Hierarchical classifier</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Complex filter response</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Support vector machine</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fingerprint classification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pang, Liaojun</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Jimin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tian, Jie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Mobascher, Arian ELSEVIER</subfield><subfield code="t">Association between dopa decarboxylase gene variants and borderline personality disorder</subfield><subfield code="d">2014</subfield><subfield code="d">the journal of the Pattern Recognition Society</subfield><subfield code="g">Amsterdam</subfield><subfield code="w">(DE-627)ELV017326583</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:46</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:12</subfield><subfield code="g">pages:3186-3197</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.patcog.2013.05.008</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">46</subfield><subfield code="j">2013</subfield><subfield code="e">12</subfield><subfield code="h">3186-3197</subfield><subfield code="g">12</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">000</subfield></datafield></record></collection>
|
score |
7.398429 |