nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN

verfasst von : Yikui Zhai, He Cao, Lu Cao, Hui Ma, Junyin Gan, Junying Zeng, Vincenzo Piuri, Fabio Scotti, Wenbo Deng, Yihang Zhi, Jinxin Wang

Erschienen in: Biometric Recognition

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Traditional feature extraction methods, such as Gabor filter and competitive coding, have been widely used in finger-knuckle-print (FKP) recognition. However, these methods focus on manually designed features which may not achieve satisfying results on FKP images. In order to solve this problem, a novel batch-normalized Convolutional Neural Network (CNN) architecture with data augmentation for FKP recognition is proposed. Firstly, a novel batch-normalized CNN is designed specifically for FKP recognition. Then, random histogram equalization is adopted as data augmentation here for training the CNN in FKP recognition. Meanwhile, batch-normalization is adopted to avoid overfitting during network training. Extensive experiments performed on the PolyU FKP database show that compared with traditional feature extraction method, the proposed method can not only extract more discriminative features, but also improve the accuracy of FKP recognition.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Dorsal Hand Vein Recognition Method Based on Multi-bit Planes Optimization

Nächstes Kapitel A New Hand Shape Recognition Algorithm Based on Delaunay Triangulation

Fei, L., Wen, J., Zhang, Z., et al.: Local multiple directional pattern of palmprint image, 3013–3018. In: The 23rd International Conference on Pattern Recognition (ICPR) (2016)

Bapat, A., Kanhangad, V.: Segmentation of hand from cluttered backgrounds for hand geometry biometrics. In: IEEE Region 10 Symposium (TENSYMP), pp. 1–4 (2017)

Chatterjee, A., Bhatia, V., Prakash, S.: Anti-spoof touchless 3D fingerprint recognition system using single shot fringe projection and biospeckle analysis. Opt. Lasers Eng. 95, 1–7 (2017)CrossRef

Huang, D., Zhang, R., Yin, Y., et al.: Local feature approach to dorsal hand vein recognition by centroid-based circular key-point grid and fine-grained matching. Image Vis. Comput. 58, 266–277 (2017)CrossRef

Zhang, L., Zhang, L., Zhang, D., et al.: Online finger-knuckle-print verification for personal authentication. Pattern Recogn. 43(7), 2560–2571 (2010)CrossRef

Zhang, L., Zhang, L., Zhang, D., et al.: Ensemble of local and global information for finger-knuckle-print recognition. Pattern Recogn. 44(9), 1990–1998 (2011)CrossRef

Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Inf. Forensics Secur. 4(1), 98–110 (2009)CrossRef

Woodard, D.L, Flynn, P.J.: Personal identification utilizing finger surface features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1030–1036 (2005)

Woodard, D.L., Flynn, P.J.: Finger surface as a biometric identifier. Comput. Vis. Image Underst. 100(3), 357–384 (2005)CrossRef

10.

Ravikanth, C., Kumar, A.: Biometric authentication using finger-back surface. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007)

11.

Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. IEEE Trans. Inf. Forensics Secur. 4(1), 98–110 (2009)CrossRef

12.

Zhang, L., Zhang, L., Zhang, D.: Finger-knuckle-print: a new biometric identifier. In: IEEE International Conference on Image Processing (ICIP), pp. 1981–1984 (2009)

13.

Zhang, L., Zhang, L., Zhang, D.: Finger-knuckle-print verification based on band-limited phase-only correlation. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 141–148. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03767-2_17CrossRef

14.

Morales, A., Travieso, C.M., Ferrer, M.A., et al.: Improved finger-knuckle-print authentication based on orientation enhancement. Electron. Lett. 47(6), 380–381 (2011)CrossRef

15.

Le, Z.: Finger knuckle print recognition based on surf algorithm. In: The Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 3, pp. 1879–1883 (2011)

16.

Badrinath, G.S., Nigam, A., Gupta, P.: An efficient finger-knuckle-print based recognition system fusing SIFT and SURF matching scores. In: Qing, S., Susilo, W., Wang, G., Liu, D. (eds.) ICICS 2011. LNCS, vol. 7043, pp. 374–387. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25243-3_30CrossRef

17.

Li, Z., Wang, K., Zuo, W.: Finger-knuckle-print recognition using local orientation feature based on steerable filter. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 224–230. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31837-5_33CrossRef

18.

Yang, W., Sun, C., Zhang, L.: A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn. 44(8), 1649–1657 (2011)CrossRef

19.

Zhang, L., Li, H.: Encoding local image patterns using riesz transforms: with applications to palmprint and finger-knuckle-print recognition. Image Vis. Comput. 30(12), 1043–1051 (2012)MathSciNetCrossRef

20.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105(2012)

21.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML), pp. 448–456 (2015)

22.

Yu, H., Yang, G., Wang, Z., et al.: A new finger-knuckle-print ROI extraction method based on two-stage center point detection. Int. J. Sig. Process. Image Proc. Pattern Recogn. 8(2), 185–200 (2015)

23.

Meraoumia, A., Chitroub, S., Bouridane, A.: Palmprint and finger-knuckle-print for efficient person recognition based on log-gabor filter response. Analog Integr. Circ. Sig. Process 69(1), 17–27 (2011)CrossRef

24.

Xiong, M., Yang, W., Sun, C.: Finger-knuckle-print recognition using LGBP. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) ISNN 2011. LNCS, vol. 6676, pp. 270–277. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21090-7_32CrossRef

25.

PolyU Finger-Knuckle-Print Database: http://www.comp.polyu.edu.hk/~biometrics (2010)

26.

Kirthiga, R., Ramesh, G.: Efficient FKP based recognition system using k-mean clustering for security system. SSRG Int. J. Electron. Commun. Eng. 1–7 (2016)

27.

Konda, K., Bouthillier, X., Memisevic, R., et al.: Dropout as data augmentation. Computer. Science 1050, 29 (2015)

28.

Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH

29.

Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678 (2014)

30.

Leung, H., Haykin, S.: The complex backpropagation algorithm. IEEE Trans. Signal Process. 39(9), 2101–2104 (1991)CrossRef

31.

Sun, Z., Tan, T., Wang, Y., et al. Ordinal palmprint representation for personal identification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 279–284 (2005)

32.

Guo, Z., Zhang, D., Zhang, L., et al.: Palmprint verification using binary orientation co-occurrence vector. Pattern Recogn. Lett. 30(13), 1219–1227 (2009)CrossRef

33.

Jia, W., Huang, D.S., Zhang, D.: Palmprint verification based on robust line orientation code. Pattern Recogn. 41(5), 1504–1513 (2008)CrossRef

34.

Wright, J., Yang, A.Y., Ganesh, A., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRef

35.

Kong, A.W.K., Zhang, D.: Competitive coding scheme for palmprint verification. In: The 17th International Conference on Pattern Recognition (ICIR), vol. 1, pp. 520–523 (2004)

Titel: A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN
verfasst von: Yikui Zhai
He Cao
Lu Cao
Hui Ma
Junyin Gan
Junying Zeng
Vincenzo Piuri
Fabio Scotti
Wenbo Deng
Yihang Zhi
Jinxin Wang
Verlag: Springer International Publishing
Buch: Biometric Recognition
Print ISBN: 978-3-319-97908-3

Electronic ISBN: 978-3-319-97909-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-97909-0_2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"