Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
One-Round Strong Oblivious Signature-Based Envelope Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Biometric Access Control with High Dimensional Facial Features

verfasst von : Ying Han Pang, Ean Yee Khor, Shih Yin Ooi

Erschienen in: Information Security and Privacy

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Access control is vital to prevent adversary from stealing resources from data centres. The security of traditional authentication means, such as password and Personal Identification Number (PIN), are imperfect for access control. In this paper, a reliable facial biometric access control with promising authentication performance is proposed. In our study, facial feature representation is computed based on ICA modelling, descriptor binarization, bitwise operation on the bit maps and effective compression via whitening PCA. The proposed technique is namely Binarized Independent Component Pattern (BICP). BICP training module integrates ICA methodology to construct ICA filter bank from natural image patches. Each face image is convoluted with the filters for the corresponding ICA responses. The ICA responses are further processed via feature binarization, and XOR bitwise operation before convert to code map. Next, block-wise histogramming is applied on each code map. By concatenating the regional histograms, it produces a set of high dimensional BICP descriptor, which will be further scaled and compressed. Empirical results show the remarkable performance of BICP on facial expression, illumination, time span and facial makeup effects.

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 Reducing the Key Size of the SRP Encryption Scheme
Nächstes Kapitel Security Analysis on Privacy-Preserving Cloud Aided Biometric Identification Schemes
Literatur
1.
Sonal, S.A., Dhiraj, P., Pallavi, D., Yogesh, H.D.: Hardware implementation of palm vein biometric modality for access control in multilayered security system. In: Second International Symposium on Computer Vision and the Internet, pp. 492–498 (2015)
2.
Zhang, L., Zhang, L., Zhang, D., Guo, Z.: Phase congruency induced local features for finger-knuckle-print recognition. ELSEVIERScienceDirect Pattern Recogn. 45, 2522–2531 (2012)CrossRef
3.
Wang, J.G., Yau, W.Y., Suwandy, A., Sung, E.: Person recognition by fusing palmprint and palm vein images based on “Laplacianpalm” representation. ELSEVIER-ScienceDirect Pattern Recogn. 41, 1514–1527 (2008)CrossRefMATH
4.
Karl, F.: HSBC uses biometrix to protect data. Infosecurity 5(8), 9 (2008)CrossRef
5.
Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3025–3032 (2013)
6.
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefMATH
7.
Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the use of SIFT features for face authentication. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 35 (2006)
8.
Déniz, O., Bueno, G., Salido, J., De la Torre, F.: Face recognition using histograms of oriented gradients. Pattern Recogn. Lett. 32(12), 1598–1603 (2011)CrossRef
9.
Shen, L., Bai, L.: A review on Gabor wavelets for face recognition. J. Pattern Anal. Appl. 9(2–3), 273–292 (2006)MathSciNetCrossRef
10.
Cao, Z., Yin, Q., Tang, X., Sun, J.: Face recognition with learning-based descriptor. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2707–2714 (2010)
11.
Hussain, S.U., Napoléon, T., Jurie, F.: Face recognition using local quantized patterns. In: British Machive Vision Conference, 11 p. (2012)
12.
Barkan, O., Weill, J., Wolf, L., Aronowitz, H.: Fast high dimensional vector multiplication face recognition. In: IEEE International Conference on Computer Vision (ICCV), pp. 1960–1967 (2013)
13.
Kannala, J., Rahtu, E.: Bsif: Binarized statistical image features. In: 21st International Conference on Pattern Recognition (ICPR), pp. 1363–1366 (2012)
14.
Ylioinas, J., Kannala, J., Hadid, A., Pietikäinen, M.: Face recognition using smoothed high-dimensional representation. In: Paulsen, R.R., Pedersen, K.S. (eds.) SCIA 2015. LNCS, vol. 9127, pp. 516–529. Springer, Heidelberg (2015)CrossRef
15.
van Hateren, J.H., van der Schaaf, A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. Royal Soc. London B: Biol. Sci. 265(1394), 359–366 (1998)CrossRef
16.
Lu, J., Liong, V.E., Zhou, X., Zhou, J.: Learning compact binary face descriptor for face recognition. IEEE Trans. Pattern Anal. Machine Intell. 37(10), 2041–2056 (2015)CrossRef
17.
Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 817–824 (2011)
18.
Trzcinski, T., Lepetit, V.: Efficient discriminative projections for compact binary descriptors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 228–242. Springer, Heidelberg (2012)CrossRef
19.
Hyvärinen, A., Hurri, J., Hoyer, P. O.: Natural Image Statistics: A Probabilistic Approach to Early Computational Vision, vol. 39. In: Springer Science and Business Media (2009)
20.
Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition. In: Tenth IEEE International Conference on Computer Vision, vol. 1, pp. 786–791 (2005)
21.
Maturana, D., Mery, D., Soto, A.: Learning discriminative local binary patterns for face recognition. In: IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011), pp. 470–475 (2011)
22.
Lei, Z., Pietikainen, M., Li, S.Z.: Learning discriminant face descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 289–302 (2014)CrossRef
23.
Ahonen, T., Rahtu, E., Ojansivu, V., Heikkilä, J.: Recognition of blurred faces using local phase quantization. In: 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
24.
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Proc. 19(6), 1635–1650 (2010)MathSciNetCrossRef
25.
Vu, N.S., Caplier, A.: Enhanced patterns of oriented edge magnitudes for face recognition and image matching. IEEE Trans. Image Proc. 21(3), 1352–1365 (2012)MathSciNetCrossRef
26.
Dantcheva, A., Chen, C., Ross, A.: Makeup challenges automated face recognition systems. In: SPIE Newsroom, pp. 1–4 (2013)
27.
Wen, L., Guo, G.D.: Dual attributes for face verification robust to facial cosmetics. J Comput. Vis. Image Process. 3(1), 63–73 (2013)MathSciNet
28.
Dantcheva, A., Chen, C., Ross, A.: Can facial cosmetics affect the matching accuracy of face recognition systems?. In: IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, pp. 391–398 (2012)
29.
Guo, G., Wen, L., Yan, S.: Face Authentication with makeup changes. IEEE Trans. Circ. Syst. Video Technol. 24(5), 814–825 (2014)CrossRef
Metadaten
Titel
Biometric Access Control with High Dimensional Facial Features
verfasst von
Ying Han Pang
Ean Yee Khor
Shih Yin Ooi
Copyright-Jahr
2016
Buch
Information Security and Privacy

Print ISBN: 978-3-319-40366-3

Electronic ISBN: 978-3-319-40367-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-40367-0_28

Premium Partner

    Bildnachweise