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:
An Overview of Spectral Imaging of Human Skin Toward Face Recognition Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

12. Face Recognition with RGB-D Images Using Kinect

verfasst von : Gaurav Goswami, Mayank Vatsa, Richa Singh

Erschienen in: Face Recognition Across the Imaging Spectrum

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

Face Recognition is one of the most extensively researched problems in biometrics, and many techniques have been proposed in the literature. While the performance of automated algorithms is close to perfect in constrained environments with controlled illumination, pose, and expression variations, recognition in unconstrained environments is still difficult. To mitigate the effect of some of these challenges, researchers have proposed to utilize 3D images which can encode much more information about the face than 2D images. However, due to sensor cost, 3D face images are expensive to capture. On the other hand, RGB-D images obtained using consumer-level devices such as the Kinect, which provide pseudo-depth data in addition to a visible spectrum color image, have a trade-off between quality and cost. In this chapter, we discuss existing RGB-D face recognition algorithms and present a state-of-the-art algorithm based on extracting discriminatory features using entropy and saliency from RGB-D images. We also present an overview of available RGB-D face datasets along with experimental results and analysis to understand the various facets of RGB-D face 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 Face Recognition in Challenging Environments: An Experimental and Reproducible Research Survey
Nächstes Kapitel Blending 2D and 3D Face Recognition
Fußnoten
1
In this chapter, we refer to the Microsoft Kinect sensor as Kinect.
 
Literatur
1.
Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2d and 3d face recognition: a survey. Pattern Recogn. Lett. 28(14), 1885–1906 (2007)CrossRef
2.
Ahonen, T., Hadid, A., Pietikainen, M.: Face recognition with local binary patterns. In: European Conference on Computer Vision, pp. 469–481 (2004)
3.
Bai, Y., Guo, L., Jin, L., Huang, Q.: A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: International Conference on Image Processing, pp. 3305–3308 (2009)
4.
Berretti, S., Del Bimbo, A., Pala, P.: Sparse matching of salient facial curves for recognition of 3-d faces with missing parts. IEEE Trans. Inf. Forensics Secur. 8(2), 374–389 (2013)CrossRef
5.
Berretti, S., Pala, P., Del Bimbo, A.: Face recognition by super-resolved 3D models from consumer depth cameras. IEEE Trans. Inf. Forensics Secur. 9(9), 1436–1449 (2014)CrossRef
6.
Besl, P.J., Jain, R.C.: Invariant surface characteristics for 3D object recognition in range images. Comput. Vis. Graph. Image Processing 33(1), 33–80 (1986)CrossRefMATH
7.
Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Robotics-DL Tentative, pp. 586–606 (1992)
8.
Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches to three-dimensional face recognition. In: International Conference on Pattern Recognition, vol. 1, pp. 358–361 (2004)
9.
Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K.: Facewarehouse: a 3D facial expression database for visual computing. IEEE Trans. Visual Comput. Graphics 20(3), 413–425 (2014)CrossRef
10.
Ciaccio, C., Wen, L., Guo, G.: Face recognition robust to head pose changes based on the RGB-D sensor. In: IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2013)
11.
Corvee, E., Bremond, F.: Body parts detection for people tracking using trees of histogram of oriented gradient descriptors. In: Advanced Video and Signal-Based Surveillance, pp. 469– 475 (2010)
12.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
13.
Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Ann. Rev. Neuroscience 18(1), 193–222 (1995)CrossRef
14.
Engelhard, N., Endres, F., Hess, J., Sturm, J., Burgard, W.: Real-time 3D visual slam with a hand-held RGB-D camera. In: RGB-D Workshop, European Robotics Forum (2011)
15.
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3D face analysis. Int. J. Comput. Vision 101(3), 437–458 (2013)CrossRef
16.
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRef
17.
Goswami, G., Bharadwaj, S., Vatsa, M., Singh, R.: On RGB-D face recognition using kinect. In: IEEE International Conference on Biometrics: Theory, Applications, and Systems (2013)
18.
Goswami, G., Vatsa, M., Singh, R.: RGB-D face recognition with texture and attribute features. IEEE Trans. Inf. Forensics Secur. 9(10), 1629–1640 (2014)CrossRef
19.
Gupta, S., Castleman, K., Markey, M., Bovik, A.: Texas 3D face recognition database. In: IEEE Southwest Symposium on Image Analysis & Interpretation, pp. 97–100 (2010)
20.
Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D.: RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments. In: International Symposium on Experimental Robotics, vol. 20, pp. 22–25 (2010)
21.
Hg, R., Jasek, P., Rofidal, C., Nasrollahi, K., Moeslund, T.B., Tranchet, G.: An RGB-D database using Microsoft’s Kinect for windows for face detection. In: International Conference on Signal Image Technology and Internet Based Systems, pp. 42–46 (2012)
22.
Ho, T.K.: Random decision forests. In: International Conference on Document Analysis and Recognition, pp. 278–282 (1995)
23.
Holz, D., Holzer, S., Rusu, R., Behnke, S.: Real-time plane segmentation using RGB-D cameras. RoboCup 2011, 306–317 (2012)
24.
Hsu, G.S., Liu, Y.L., Peng, H.C., Wu, P.X.: RGB-D based face reconstruction and recognition. IEEE Trans. Info. Forensics Secur. 9(12), 2110–2118 (2014)
25.
Huang, Y., Wang, Y., Tan, T.: Combining statistics of geometrical and correlative features for 3D face recognition. In: British Machine Vision Conference, pp. 879–888 (2006)
26.
Huynh, T., Min, R., Dugelay, J.L.: An efficient LBP-based descriptor for facial depth images applied to gender recognition using RGB-D face data. In: Asian Conference on Computer Vision (2012)
27.
Itti, L., Koch, C.: Computational modeling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)
28.
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)CrossRef
29.
Jain, A., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recogn. 38(12), 2270–2285 (2005)
30.
Jain, A.K., Li, S.Z.: Handbook of face recognition. Springer, New York, Inc. (2005)
31.
Kemelmacher-Shlizerman, I., Basri, R.: 3D face reconstruction from a single image using a single reference face shape. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 394–405 (2011)
32.
Kim, D., Park, J., Kak, A.C.: Estimating head pose with an RGB-D sensor: a comparison of appearance-based and pose-based local subspace methods. In: International Conference on Image Processing, pp. 3637–3641 (2013)
33.
Kovashka, A., Parikh, D., Grauman, K.: Whittlesearch: image search with relative attribute feedback. In: In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2973–2980 (2012)
34.
Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: International Conference on Computer Vision, pp. 365–372 (2009)
35.
Kun, D., Parikh, D., Crandall, D., Grauman, K.: Discovering localized attributes for fine-grained recognition. In: Computer Vision and Pattern Recognition, pp. 3474–3481 (2012)
36.
Li, B.Y.L., Mian, A.S., Liu, W., Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: Workshop on the Applications of Computer Vision, pp. 186–192 (2013)
37.
Li, S., Ngan, K.N., Sheng, L.: A head pose tracking system using RGB-D camera. In: Computer Vision Systems, pp. 153–162 (2013)
38.
Lowe, D.G.: Object recognition from local scale-invariant features. In: International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
39.
Mian, A.: Illumination invariant recognition and 3D reconstruction of faces using desktop optics. Opt. Express 19(8), 7491–7506 (2011)
40.
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954 (2005)
41.
Ramey, A., Gonzalez-Pacheco, V., Salichs, M.A.: Integration of a low-cost RGB-D sensor in a social robot for gesture recognition. In: Human-Robot Interaction, pp. 229–230 (2011)
42.
Rrnyi, A.: On measures of entropy and information. In: Berkeley Symposium on Mathematical Statistics and Probability, pp. 547–561 (1961)
43.
Segundo, M.P., Sarkar, S., Goldgof, D., Bellon, L.S.O.: Continuous 3D face authentication using RGB-D cameras. In: Computer Vision and Pattern Recognition Biometrics Workshop (2013)
44.
Tuzel, O., Porikli, F., Meer, P.: Region covariance: A fast descriptor for detection and classification. In: European Conference on Computer Vision, pp. 589–600 (2006)
45.
Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing. I. theory. IEEE Trans. Signal Process. 41(2), 821–833 (1993)
46.
Wang, C., Guan, L.: Graph cut video object segmentation using histogram of oriented gradients. In: International Symposium on Circuits and Systems, pp. 2590–2593 (2008)
47.
Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1), 37–52 (1987)
48.
Wolf, L., Hassner, T., Taigman, Y., et al.: Descriptor based methods in the wild. In: European Conference on Computer Vision Real Faces Workshop (2008)
49.
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)
50.
Yang, J., Liu, C.: Color image discriminant models and algorithms for face recognition. IEEE Trans. Neural Netw. 19(12), 2088–2098 (2008)
51.
Ye, J., Janardan, R., Li, Q.: Two-dimensional linear discriminant analysis. In: Advances in Neural Information Processing Systems, pp. 1569–1576 (2004)
52.
Zhang, L., Chen, J., Lu, Y., Wang, P.: Face recognition using scale invariant feature transform and support vector machine. In: International Conference for Young Computer Scientists, pp. 1766–1770 (2008)
53.
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2879–2886. IEEE (2012)
Metadaten
Titel
Face Recognition with RGB-D Images Using Kinect
verfasst von
Gaurav Goswami
Mayank Vatsa
Richa Singh
Copyright-Jahr
2016
Buch
Face Recognition Across the Imaging Spectrum

Print ISBN: 978-3-319-28499-6

Electronic ISBN: 978-3-319-28501-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-28501-6_12

Premium Partner

    Bildnachweise