Fingerprint pattern identification and classification approach based on convolutional neural networks
Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were classified into four categories to speed up recognition, but the method of that classification is not suitable for researching the diversity of hu...
Saved in:
| Published in: | Neural computing & applications Vol. 32; no. 10; pp. 5725 - 5734 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Springer London
01.05.2020
Springer Nature B.V |
| Subjects: | |
| ISSN: | 0941-0643, 1433-3058 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were classified into four categories to speed up recognition, but the method of that classification is not suitable for researching the diversity of human personalities. Therefore, in this paper, fingerprint patterns were classified into six types and the accuracy of the recognition was improved to facilitate the research on human personality characteristics. Based on this idea, a six-category fingerprint database is annotated manually and a convolutional neural network (CNN) is proposed for identifying real fingerprint patterns. The new CNN consists of four convolutional layers, three max-pooling layers, two norm layers, and three fully connected layers. The best accuracy the model achieved was 94.87% for a six-category fingerprint database and 92.9% accuracy for a four-category fingerprint database. The results of experimental tests show that the proposed model can recognize the pattern features from a large fingerprint database using the automatic learning and feature extraction abilities of the CNN to get a greater accuracy than in previous experiments. |
|---|---|
| AbstractList | Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were classified into four categories to speed up recognition, but the method of that classification is not suitable for researching the diversity of human personalities. Therefore, in this paper, fingerprint patterns were classified into six types and the accuracy of the recognition was improved to facilitate the research on human personality characteristics. Based on this idea, a six-category fingerprint database is annotated manually and a convolutional neural network (CNN) is proposed for identifying real fingerprint patterns. The new CNN consists of four convolutional layers, three max-pooling layers, two norm layers, and three fully connected layers. The best accuracy the model achieved was 94.87% for a six-category fingerprint database and 92.9% accuracy for a four-category fingerprint database. The results of experimental tests show that the proposed model can recognize the pattern features from a large fingerprint database using the automatic learning and feature extraction abilities of the CNN to get a greater accuracy than in previous experiments. |
| Author | Guo, Xiaomeng Wu, Fan Zhu, Juelin |
| Author_xml | – sequence: 1 givenname: Fan orcidid: 0000-0001-9392-2597 surname: Wu fullname: Wu, Fan email: wufan@hnu.edu.cn organization: School of Information Science and Engineering, Hunan University – sequence: 2 givenname: Juelin surname: Zhu fullname: Zhu, Juelin organization: School of Information Science and Engineering, Hunan University – sequence: 3 givenname: Xiaomeng surname: Guo fullname: Guo, Xiaomeng organization: School of Information Science and Engineering, Hunan University |
| BookMark | eNp9kE1LAzEQhoNUsFb_gKcFz6vJJvuRoxSrQsGLnkM-NXVN1iRr8d-bdgXFQ08DM_MM7zynYOa80wBcIHiFIGyvI4R1hUqIaAkJobTcHoE5IhiXGNbdDMwhJXncEHwCTmPcQAhJ09VzoFfWvegwBOtSMfCUdHCFVdola6zkyXpXcKcK2fMY_7SGIXguXwvBo1ZF7kjvPn0_7qa8L5wew76krQ9v8QwcG95Hff5TF-B5dfu0vC_Xj3cPy5t1KTGiqaRIGy4INhArQ1ojatUaTYU2FRKSU6JJrRpE6pZiLBRRWBjVNp3hpOtqUeMFuJzu5nQfo46JbfwYcqDIqoqiroK0gXmrmrZk8DEGbVh-_52HL4Yg2-lkk06WdbK9TrbNUPcPkjbtZaTAbX8YxRMad5qz7d9UB6hvu5OQEQ |
| CitedBy_id | crossref_primary_10_1002_cpe_6057 crossref_primary_10_3390_app15052793 crossref_primary_10_3390_math9070730 crossref_primary_10_1002_eng2_12897 crossref_primary_10_1007_s42979_024_02885_3 crossref_primary_10_1016_j_patcog_2025_111439 crossref_primary_10_1155_2022_1101282 crossref_primary_10_1109_JSEN_2021_3077698 crossref_primary_10_3390_app13021188 crossref_primary_10_1016_j_compeleceng_2021_107387 crossref_primary_10_1155_acis_8442143 crossref_primary_10_4028_p_fiJ4gb crossref_primary_10_1007_s00371_022_02437_x crossref_primary_10_1016_j_patrec_2023_08_006 |
| Cites_doi | 10.1016/S0031-3203(01)00121-2 10.1109/ACCESS.2017.2788044 10.1016/S0262-8856(01)00045-2 10.1016/0031-3203(84)90079-7 10.1016/j.neucom.2017.08.062 10.1016/j.patrec.2007.04.002 10.1109/TII.2019.2909473 10.1109/FSKD.2017.8393034 10.1109/TGRS.2009.2037898 10.1007/3-540-45344-X_37 10.1006/ciun.1993.1012 10.1016/B978-1-4832-3093-1.50017-1 10.1016/0031-3203(95)00106-9 10.1109/34.761265 10.1109/TSMC.2017.2690673 10.1007/s10916-016-0436-2 10.1109/TPDS.2018.2877359 10.1145/2647868.2654889 10.1109/TIP.2012.2195014 10.1016/j.patcog.2007.07.004 10.1109/IJCNN.2011.6033589 10.1109/5.726791 10.1109/TPAMI.1980.4767009 10.1016/0031-3203(75)90011-4 10.1016/j.patcog.2013.05.008 10.1109/ICPR.1996.547013 10.1109/TIP.2011.2177846 |
| ContentType | Journal Article |
| Copyright | Springer-Verlag London Ltd., part of Springer Nature 2019 Springer-Verlag London Ltd., part of Springer Nature 2019. |
| Copyright_xml | – notice: Springer-Verlag London Ltd., part of Springer Nature 2019 – notice: Springer-Verlag London Ltd., part of Springer Nature 2019. |
| DBID | AAYXX CITATION 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS |
| DOI | 10.1007/s00521-019-04499-w |
| DatabaseName | CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Proquest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China |
| DatabaseTitle | CrossRef Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Advanced Technologies & Aerospace Collection |
| Database_xml | – sequence: 1 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 5734 |
| ExternalDocumentID | 10_1007_s00521_019_04499_w |
| GrantInformation_xml | – fundername: National Key Research and Development Program of China grantid: 2016YFB0201800 – fundername: Hunan Provincial Key Research and Development Program grantid: 2018GK2055 |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB DWQXO PKEHL PQEST PQQKQ PQUKI PRINS |
| ID | FETCH-LOGICAL-c319t-91efab43f03df47fb5d7fe9bef21bca94e45d61457933bd4d3bfd768fa4885b53 |
| IEDL.DBID | P5Z |
| ISICitedReferencesCount | 15 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000529745200034&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0941-0643 |
| IngestDate | Wed Nov 05 00:42:46 EST 2025 Sat Nov 29 02:59:13 EST 2025 Tue Nov 18 21:31:35 EST 2025 Fri Feb 21 02:35:54 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 10 |
| Keywords | Pattern feature Fingerprint Identification Convolutional neural network |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-91efab43f03df47fb5d7fe9bef21bca94e45d61457933bd4d3bfd768fa4885b53 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-9392-2597 |
| PQID | 2291820960 |
| PQPubID | 2043988 |
| PageCount | 10 |
| ParticipantIDs | proquest_journals_2291820960 crossref_primary_10_1007_s00521_019_04499_w crossref_citationtrail_10_1007_s00521_019_04499_w springer_journals_10_1007_s00521_019_04499_w |
| PublicationCentury | 2000 |
| PublicationDate | 20200500 2020-05-00 20200501 |
| PublicationDateYYYYMMDD | 2020-05-01 |
| PublicationDate_xml | – month: 5 year: 2020 text: 20200500 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2020 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | Jain AK, Minut S (2002) Hierarchical kernel fitting for fingerprint classification and alignment. In: Object recognition supported by user interaction for service robots, vol 2. IEEE, pp 469–473 ChenCLiKOuyangATangZLiKGpu-accelerated parallel hierarchical extreme learning machine on Flink for big dataIEEE Trans Syst Man Cybern Syst201747102740275310.1109/TSMC.2017.2690673 KawagoeMTojoAFingerprint pattern classificationPattern Recogn198417329530310.1016/0031-3203(84)90079-7 GiacintoGRoliFDesign of effective neural network ensembles for image classification purposesImage Vis Comput2001199–1069970710.1016/S0262-8856(01)00045-2 LecunYLBottouLBengioYGradient-based learning applied to document recognitionProc IEEE199886112278232410.1109/5.726791 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 CaoKPangLLiangJFingerprint classification by a hierarchical classifierPattern Recogn201346123186319710.1016/j.patcog.2013.05.008 Maio D, Maltoni D (1996) A structural approach to fingerprint classification. In: Proceedings of the 13th international conference on, pp 578–585 Duan M, Li K, Tian Q (2018) A novel multi-task tensor correlation neural network for facial attribute prediction. arXiv preprint arXiv:1804.02810 Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568-576 BaiXNiwasSILinWJuBFKwohCKWangLLearning ECOC code matrix for multiclass classification with application to glaucoma diagnosisJ Med Syst20164047810.1007/s10916-016-0436-2 Dai J, Li Y, He K et al (2016) R-FCN: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387 WangLDaiMApplication of a new type of singular points in fingerprint classificationPattern Recogn Lett200728131640165010.1016/j.patrec.2007.04.002 ZhangLWangLLinWConjunctive patches subspace learning with side information for collaborative image retrievalIEEE Trans Image Process20122183707372029604591373.9448610.1109/TIP.2012.2195014 Chen C et al (2018) Exploiting spatio-temporal correlations with multiple 3D convolutional neural networks for citywide vehicle flow prediction. In: 2018 IEEE international conference on data mining (ICDM). IEEE, pp 893–898 ZhangLWangLLinWSemisupervised biased maximum margin analysis for interactive image retrievalIEEE Trans Image Process20122142294230829595291373.9448510.1109/TIP.2011.2177846 ChenJA bi-layered parallel training architecture for large-scale convolutional neural networksIEEE Trans Parallel Distrib Syst20183096597610.1109/TPDS.2018.2877359 Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105 Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 international joint conference on neural networks, San Jose, CA, pp 2809–2813 Wang R, Han C, Wu Y et al (2014) Fingerprint classification based on depth neural network. arXiv preprint arXiv:1409.5188 KaruKJainAKFingerprint classificationPattern Recogn199629338940410.1016/0031-3203(95)00106-9 HongJHMinJKChoUKFingerprint classification using one-vs-all support vector machines dynamically ordered with näive Bayes classifiersPattern Recogn20084126626711131.6851310.1016/j.patcog.2007.07.004 Bowen JD (1992) The home office automatic fingerprint pattern classification project. In: IEE Colloquium on neural networks for image processing applications. IET, pp 1/1–1/5 Sermanet P, Chintala S, LeCun Y (2012) Convolutional neural networks applied to house numbers digit classification. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), Tsukuba, pp 3288–3291 DonahueMJRokhlinSIOn the use of level curves in image analysisCVGIP Image Underst199357218520310.1006/ciun.1993.1012 YaoYFrasconiPPontilMKanadeTJainARathaNKFingerprint classification with combinations of support vector machinesInternational conference on audio-and video-based biometric person authentication2001BerlinSpringer25325810.1007/3-540-45344-X_37 Jia Y, Shelhamer E, Donahue J et al (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 675–678 KerJWangLRaoJDeep learning applications in medical image analysisIEEE Access201869375938910.1109/ACCESS.2017.2788044 JainAKPrabhakarSHongLA multichannel approach to fingerprint classificationIEEE Trans Pattern Anal Mach Intell200221434835910.1109/34.761265 ChangJHFanKCA new model for fingerprint classification by ridge distribution sequencesPattern Recogn2002356120912231001.6892510.1016/S0031-3203(01)00121-2 RaoKBalckKType classification of fingerprints: a syntactic approachIEEE Trans Pattern Anal Mach Intell1980232233110.1109/TPAMI.1980.4767009 GrasselliAOn the automatic classification of fingerprints—some considerations on the linguistic interpretation of picturesMethodol Pattern Recogn1969588125327310.1016/B978-1-4832-3093-1.50017-1 ChenJLiKDengQLiKPhilipSYDistributed deep learning model for intelligent video surveillance systems with edge computingIEEE Trans Ind Inf201910.1109/TII.2019.2909473 Dai T, Li K, Chen C (2017) A parallel randomized neural network on in-memory cluster computing for big data. In: 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD). IEEE, pp 1769–1778 ZeilerMDFergusRFleetDPajdlaTSchieleBTuytelaarsTVisualizing and understanding convolutional networksEuropean conference on computer vision2014ChamSpringer818833 MoayerBFuKSA syntactic approach to fingerprint pattern recognitionPattern Recogn197571–21230318.6805910.1016/0031-3203(75)90011-4 DuanMLiKYangCLiKA hybrid deep learning CNN–ELM for age and gender classificationNeurocomputing201827544846110.1016/j.neucom.2017.08.062 HenryERClassification and uses of finger prints1905RichmondHM Stationery Office Gholami A, Azad A, Jin P, Keutzer K, Buluc A (2017) Integrated model, batch and domain parallelism in training neural networks. arXiv preprint arXiv:1712.04432 RatleFCamps-VallsGWestonJSemisupervised neural networks for efficient hyperspectral image classificationIEEE Trans Geosci Remote Sens20104852271228210.1109/TGRS.2009.2037898 A Grasselli (4499_CR23) 1969; 588 K Cao (4499_CR17) 2013; 46 4499_CR10 4499_CR33 YL Lecun (4499_CR40) 1998; 86 ER Henry (4499_CR7) 1905 4499_CR2 4499_CR18 M Duan (4499_CR25) 2018; 275 4499_CR3 4499_CR39 4499_CR5 4499_CR36 4499_CR16 L Zhang (4499_CR28) 2012; 21 4499_CR38 4499_CR1 4499_CR15 4499_CR37 B Moayer (4499_CR13) 1975; 7 4499_CR6 JH Chang (4499_CR19) 2002; 35 JH Hong (4499_CR11) 2008; 41 MJ Donahue (4499_CR22) 1993; 57 C Chen (4499_CR34) 2017; 47 M Kawagoe (4499_CR14) 1984; 17 K Karu (4499_CR8) 1996; 29 AK Jain (4499_CR21) 2002; 21 X Bai (4499_CR31) 2016; 40 L Wang (4499_CR20) 2007; 28 J Chen (4499_CR35) 2018; 30 J Ker (4499_CR30) 2018; 6 Y Yao (4499_CR9) 2001 F Ratle (4499_CR24) 2010; 48 MD Zeiler (4499_CR4) 2014 4499_CR26 G Giacinto (4499_CR29) 2001; 19 K Rao (4499_CR12) 1980; 2 J Chen (4499_CR32) 2019 L Zhang (4499_CR27) 2012; 21 |
| References_xml | – reference: ZeilerMDFergusRFleetDPajdlaTSchieleBTuytelaarsTVisualizing and understanding convolutional networksEuropean conference on computer vision2014ChamSpringer818833 – reference: Sermanet P, Chintala S, LeCun Y (2012) Convolutional neural networks applied to house numbers digit classification. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), Tsukuba, pp 3288–3291 – reference: DonahueMJRokhlinSIOn the use of level curves in image analysisCVGIP Image Underst199357218520310.1006/ciun.1993.1012 – reference: ZhangLWangLLinWConjunctive patches subspace learning with side information for collaborative image retrievalIEEE Trans Image Process20122183707372029604591373.9448610.1109/TIP.2012.2195014 – reference: GiacintoGRoliFDesign of effective neural network ensembles for image classification purposesImage Vis Comput2001199–1069970710.1016/S0262-8856(01)00045-2 – reference: BaiXNiwasSILinWJuBFKwohCKWangLLearning ECOC code matrix for multiclass classification with application to glaucoma diagnosisJ Med Syst20164047810.1007/s10916-016-0436-2 – reference: WangLDaiMApplication of a new type of singular points in fingerprint classificationPattern Recogn Lett200728131640165010.1016/j.patrec.2007.04.002 – reference: Duan M, Li K, Tian Q (2018) A novel multi-task tensor correlation neural network for facial attribute prediction. arXiv preprint arXiv:1804.02810 – reference: Dai T, Li K, Chen C (2017) A parallel randomized neural network on in-memory cluster computing for big data. In: 13th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD). IEEE, pp 1769–1778 – reference: RatleFCamps-VallsGWestonJSemisupervised neural networks for efficient hyperspectral image classificationIEEE Trans Geosci Remote Sens20104852271228210.1109/TGRS.2009.2037898 – reference: Chen C et al (2018) Exploiting spatio-temporal correlations with multiple 3D convolutional neural networks for citywide vehicle flow prediction. In: 2018 IEEE international conference on data mining (ICDM). IEEE, pp 893–898 – reference: CaoKPangLLiangJFingerprint classification by a hierarchical classifierPattern Recogn201346123186319710.1016/j.patcog.2013.05.008 – reference: ChenCLiKOuyangATangZLiKGpu-accelerated parallel hierarchical extreme learning machine on Flink for big dataIEEE Trans Syst Man Cybern Syst201747102740275310.1109/TSMC.2017.2690673 – reference: HongJHMinJKChoUKFingerprint classification using one-vs-all support vector machines dynamically ordered with näive Bayes classifiersPattern Recogn20084126626711131.6851310.1016/j.patcog.2007.07.004 – reference: ZhangLWangLLinWSemisupervised biased maximum margin analysis for interactive image retrievalIEEE Trans Image Process20122142294230829595291373.9448510.1109/TIP.2011.2177846 – reference: LecunYLBottouLBengioYGradient-based learning applied to document recognitionProc IEEE199886112278232410.1109/5.726791 – reference: KerJWangLRaoJDeep learning applications in medical image analysisIEEE Access201869375938910.1109/ACCESS.2017.2788044 – reference: Bowen JD (1992) The home office automatic fingerprint pattern classification project. In: IEE Colloquium on neural networks for image processing applications. IET, pp 1/1–1/5 – reference: Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 international joint conference on neural networks, San Jose, CA, pp 2809–2813 – reference: Dai J, Li Y, He K et al (2016) R-FCN: Object detection via region-based fully convolutional networks. In: Advances in neural information processing systems, pp 379–387 – reference: KaruKJainAKFingerprint classificationPattern Recogn199629338940410.1016/0031-3203(95)00106-9 – reference: KawagoeMTojoAFingerprint pattern classificationPattern Recogn198417329530310.1016/0031-3203(84)90079-7 – reference: DuanMLiKYangCLiKA hybrid deep learning CNN–ELM for age and gender classificationNeurocomputing201827544846110.1016/j.neucom.2017.08.062 – reference: Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568-576 – reference: Wang R, Han C, Wu Y et al (2014) Fingerprint classification based on depth neural network. arXiv preprint arXiv:1409.5188 – reference: GrasselliAOn the automatic classification of fingerprints—some considerations on the linguistic interpretation of picturesMethodol Pattern Recogn1969588125327310.1016/B978-1-4832-3093-1.50017-1 – reference: Jain AK, Minut S (2002) Hierarchical kernel fitting for fingerprint classification and alignment. In: Object recognition supported by user interaction for service robots, vol 2. IEEE, pp 469–473 – reference: JainAKPrabhakarSHongLA multichannel approach to fingerprint classificationIEEE Trans Pattern Anal Mach Intell200221434835910.1109/34.761265 – reference: ChenJLiKDengQLiKPhilipSYDistributed deep learning model for intelligent video surveillance systems with edge computingIEEE Trans Ind Inf201910.1109/TII.2019.2909473 – reference: ChenJA bi-layered parallel training architecture for large-scale convolutional neural networksIEEE Trans Parallel Distrib Syst20183096597610.1109/TPDS.2018.2877359 – reference: Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105 – reference: Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 – reference: ChangJHFanKCA new model for fingerprint classification by ridge distribution sequencesPattern Recogn2002356120912231001.6892510.1016/S0031-3203(01)00121-2 – reference: Jia Y, Shelhamer E, Donahue J et al (2014) Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 675–678 – reference: MoayerBFuKSA syntactic approach to fingerprint pattern recognitionPattern Recogn197571–21230318.6805910.1016/0031-3203(75)90011-4 – reference: Gholami A, Azad A, Jin P, Keutzer K, Buluc A (2017) Integrated model, batch and domain parallelism in training neural networks. arXiv preprint arXiv:1712.04432 – reference: RaoKBalckKType classification of fingerprints: a syntactic approachIEEE Trans Pattern Anal Mach Intell1980232233110.1109/TPAMI.1980.4767009 – reference: Maio D, Maltoni D (1996) A structural approach to fingerprint classification. In: Proceedings of the 13th international conference on, pp 578–585 – reference: HenryERClassification and uses of finger prints1905RichmondHM Stationery Office – reference: YaoYFrasconiPPontilMKanadeTJainARathaNKFingerprint classification with combinations of support vector machinesInternational conference on audio-and video-based biometric person authentication2001BerlinSpringer25325810.1007/3-540-45344-X_37 – volume: 35 start-page: 1209 issue: 6 year: 2002 ident: 4499_CR19 publication-title: Pattern Recogn doi: 10.1016/S0031-3203(01)00121-2 – volume: 6 start-page: 9375 year: 2018 ident: 4499_CR30 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2788044 – volume: 19 start-page: 699 issue: 9–10 year: 2001 ident: 4499_CR29 publication-title: Image Vis Comput doi: 10.1016/S0262-8856(01)00045-2 – volume: 17 start-page: 295 issue: 3 year: 1984 ident: 4499_CR14 publication-title: Pattern Recogn doi: 10.1016/0031-3203(84)90079-7 – volume: 275 start-page: 448 year: 2018 ident: 4499_CR25 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.08.062 – ident: 4499_CR36 – volume: 28 start-page: 1640 issue: 13 year: 2007 ident: 4499_CR20 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2007.04.002 – year: 2019 ident: 4499_CR32 publication-title: IEEE Trans Ind Inf doi: 10.1109/TII.2019.2909473 – ident: 4499_CR37 doi: 10.1109/FSKD.2017.8393034 – volume: 48 start-page: 2271 issue: 5 year: 2010 ident: 4499_CR24 publication-title: IEEE Trans Geosci Remote Sens doi: 10.1109/TGRS.2009.2037898 – start-page: 253 volume-title: International conference on audio-and video-based biometric person authentication year: 2001 ident: 4499_CR9 doi: 10.1007/3-540-45344-X_37 – ident: 4499_CR6 – volume: 57 start-page: 185 issue: 2 year: 1993 ident: 4499_CR22 publication-title: CVGIP Image Underst doi: 10.1006/ciun.1993.1012 – start-page: 818 volume-title: European conference on computer vision year: 2014 ident: 4499_CR4 – volume: 588 start-page: 253 issue: 1 year: 1969 ident: 4499_CR23 publication-title: Methodol Pattern Recogn doi: 10.1016/B978-1-4832-3093-1.50017-1 – volume: 29 start-page: 389 issue: 3 year: 1996 ident: 4499_CR8 publication-title: Pattern Recogn doi: 10.1016/0031-3203(95)00106-9 – volume: 21 start-page: 348 issue: 4 year: 2002 ident: 4499_CR21 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/34.761265 – volume: 47 start-page: 2740 issue: 10 year: 2017 ident: 4499_CR34 publication-title: IEEE Trans Syst Man Cybern Syst doi: 10.1109/TSMC.2017.2690673 – ident: 4499_CR3 – ident: 4499_CR1 – volume: 40 start-page: 78 issue: 4 year: 2016 ident: 4499_CR31 publication-title: J Med Syst doi: 10.1007/s10916-016-0436-2 – ident: 4499_CR10 – volume: 30 start-page: 965 year: 2018 ident: 4499_CR35 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2018.2877359 – ident: 4499_CR38 doi: 10.1145/2647868.2654889 – volume: 21 start-page: 3707 issue: 8 year: 2012 ident: 4499_CR28 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2012.2195014 – volume: 41 start-page: 662 issue: 2 year: 2008 ident: 4499_CR11 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2007.07.004 – ident: 4499_CR2 doi: 10.1109/IJCNN.2011.6033589 – ident: 4499_CR5 – ident: 4499_CR39 – ident: 4499_CR33 – volume-title: Classification and uses of finger prints year: 1905 ident: 4499_CR7 – ident: 4499_CR18 – volume: 86 start-page: 2278 issue: 11 year: 1998 ident: 4499_CR40 publication-title: Proc IEEE doi: 10.1109/5.726791 – ident: 4499_CR16 – volume: 2 start-page: 223 issue: 3 year: 1980 ident: 4499_CR12 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.1980.4767009 – volume: 7 start-page: 1 issue: 1–2 year: 1975 ident: 4499_CR13 publication-title: Pattern Recogn doi: 10.1016/0031-3203(75)90011-4 – volume: 46 start-page: 3186 issue: 12 year: 2013 ident: 4499_CR17 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2013.05.008 – ident: 4499_CR15 doi: 10.1109/ICPR.1996.547013 – volume: 21 start-page: 2294 issue: 4 year: 2012 ident: 4499_CR27 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2011.2177846 – ident: 4499_CR26 |
| SSID | ssj0004685 |
| Score | 2.2991734 |
| Snippet | Fingerprint pattern recognition and classification can be of assistance in the research on human personality. In some previous studies, fingerprints were... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 5725 |
| SubjectTerms | Accuracy Advances in Parallel and Distributed Computing for Neural Computing Artificial Intelligence Artificial neural networks Biometric recognition systems Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Feature extraction Feature recognition Fingerprinting Fingerprints Image Processing and Computer Vision Model accuracy Neural networks Pattern recognition Personality Probability and Statistics in Computer Science |
| SummonAdditionalLinks | – databaseName: Springer Nature - Connect here FIRST to enable access dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDLZgcODCeIrBQDlwg0ptk6ztESEmDmhCPKbdqjQPaRIq01rY38fJ0m0gQIJTpTZ1KzuO7cT-DHBuaBpqk0QBNSIJGDq0QSHSMEDPXHOuVcId4s3wLhkM0tEou_dFYVWT7d4cSbqVelHsZncwbehrN_PRTw9m67CB5i61DRseHocr1ZCuESfGLTanh1FfKvM9jc_maOljfjkWddam3_7ff-7AtvcuydV8OuzCmi73oN10biBekfdB9x1FS7gmEwexWZKx8plDTlhElIpI61uv3PIA5MTaPkXwjk1a95MXv2vBMd3FpZZXB_Dcv3m6vg18w4VAoibWuPBpIwpGTUiVYYkpuEqMzgpt4qiQImOacYX2nKNS00IxRQujULxG4DLAC04PoVW-lvoICI0pYzIVKOyYiV4iQhOpLJSo5ILKXtaBqOF7Lj0auW2K8ZIvcJQdH3PkY-74mM86cLF4ZzLH4vh1dLcRZ-71ssrjOLOI9Ri2deCyEd_y8c_Ujv82_AS2YhuYu8zILrTq6Zs-hU35Xo-r6Zmbrx-1FeeJ priority: 102 providerName: Springer Nature |
| Title | Fingerprint pattern identification and classification approach based on convolutional neural networks |
| URI | https://link.springer.com/article/10.1007/s00521-019-04499-w https://www.proquest.com/docview/2291820960 |
| Volume | 32 |
| WOSCitedRecordID | wos000529745200034&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1433-3058 dateEnd: 20241211 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JSwMxFH5o9eDFumLdyMGbDk4nidM5iYrFg5TihngZMlmgINPajvr3fS9mrAr24mUGMksC39uSvHwP4MDxTmxd2o64U2kkMKCNCtWJI4zMrZTWpNIz3jxcp71e5_Ex64cFt0lIq6xtojfUZqhpjfw4STLiGseA-3T0ElHVKNpdDSU05mGBWBKodENfPn07F-lLcuIMhrJ7BA-HZvzROVoPpYk0bQ1g1B-9_3RM02jz1wap9zvd5n9HvALLIeJkZ58isgpztlyDZl3NgQXlXgfb9f3TMCo28rSbJRuYkE3kAWSqNExTvP2tKZCSM_KHhmELJbIHgcZ-iTDT33y6-WQD7ruXdxdXUSjCEGnUzgqNoXWqENzF3DiRukKa1NmssC5pF1plwgpp0MdLVHReGGF44QxC7hSaBllIvgmNcljaLWA84ULojkIBSIQ6SVXs2iaLNSq-4voka0G7RiDXgaGcCmU851_cyh61HFHLPWr5ewsOv74ZffJzzHx7t4YqD7o6yac4teCoBnv6-O-_bc_-2w4sJTQ599mRu9Coxq92Dxb1WzWYjPdh4fyy17_Z9xKL15vbhw-m7PN6 |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1LLwRBEK4IEi7WM5ZFHzgxMTvdY3YOIoINsTYOiLiMnn4kEhnLLht_ym9U1dtjkXBzcJqk51nTX7266wGwbnkjNDapB9zKJBBo0Aa5bIQBWuYmjo1OYlfx5qqVtNuN6-v0fATeylwYCqssZaIT1PpB0Rr5dhSlVGscDe69zmNAXaNod7VsoTGAxal57aPL1t09OcT53Yii5tHFwXHguwoECuHWQ-42VuaC25BrKxKbxzqxJs2Njeq5kqkwItaotGJELs-10Dy3GmmwErEe59QlAkX-mBD4Mcg_5_HNpzxM1wIUPSaKJhLcJ-m4VD1afyXHnbYi0MsI-l8V4dC6_bYh6_Rcs_Lf_tA0THmLmu0PWGAGRkwxC5WyWwXzwmsOTNPRS2T3WMeVFS3YnfbRUg6gTBaaKfInPg35ouuM9L1mOEKB-p5h8b1UENQdXDh9dx4u_4TYBRgtHgqzCIxHXAjVkAjwSMidRIa2rtNQoWCTXO2kVaiXM54pX4GdGoHcZx-1ox1KMkRJ5lCS9auw-XFPZ1B_5NerayU0Mi-LutkQF1XYKsE1PP3z05Z-f9oaTBxfnLWy1kn7dBkmI1qIcJGgNRjtPT2bFRhXL7277tOq4xIGt38NunfhyFCR |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dS8MwED90ivji_MTp1Dz4pmVtk67to6hFcYyBOvYW0nzAQOrYqvv3TbJ0m6KC-FRI06Tk7nJ3yd3vAM4VTnyp4sDDisUe0Qatl7PE97RlLqNIijiyiDf9TtztJoNB2lvK4rfR7tWV5CynwaA0FWVrJFRrnvhmTjONG2wO9rXN7k1XYY2YQHrjrz_2lzIjbVFO7cOY-B6CXdrM92N8Vk0Le_PLFanVPFn9__-8DVvO6kRXMzbZgRVZ7EK9quiAnIDvgczs6GaSEo0s9GaBhsJFFFkiIlYIxI3NvdTkgMmR0YkC6RYTzO6YWs9rQDPtw4acT_bhObt9ur7zXCEGj2sJLfWGKBXLCVY-ForEKo9ErGSaSxUGOWcpkSQSWs9HWthxLojAuRKa7Irp7SHKI3wAteK1kIeAcIgJ4QnTTBAS1o6ZrwKR-lwLP8O8nTYgqGhAuUMpN8UyXugcX9muI9XrSO060mkDLubfjGYYHb_2blakpU5eJzQMU4Nkr925BlxWpFy8_nm0o791P4ON3k1GO_fdh2PYDI3vboMnm1Arx2_yBNb5ezmcjE8tG38AuOLzUQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Fingerprint+pattern+identification+and+classification+approach+based+on+convolutional+neural+networks&rft.jtitle=Neural+computing+%26+applications&rft.au=Wu%2C+Fan&rft.au=Zhu+Juelin&rft.au=Guo+Xiaomeng&rft.date=2020-05-01&rft.pub=Springer+Nature+B.V&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=32&rft.issue=10&rft.spage=5725&rft.epage=5734&rft_id=info:doi/10.1007%2Fs00521-019-04499-w |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |