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...
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| Vydáno v: | Neural computing & applications Ročník 32; číslo 10; s. 5725 - 5734 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Springer London
01.05.2020
Springer Nature B.V |
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| ISSN: | 0941-0643, 1433-3058 |
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| 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. |
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| 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 |
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| 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 |
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| 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 |
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