Skip to main content
SpringerLink
Log in

Volumetric Density of Triangulated Range Images for Face Recognition

  • Conference paper
  • First Online:
Book cover Transactions on Computational Science XXXV

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 11960))

Abstract

In this paper, a volumetric space representation of 3D range face image has been established for developing a robust 3D face recognition system. A volumetric space has been created on some distinct triangular regions of the 3D range face image. Further, we have constructed 3D voxels corresponding to those regions for developing voxelization-based 3D face classification system. The proposed 3D face recognition system has mainly three parts. At first, seven significant landmarks are detected on the face. Secondly, any three individual landmarks are used to create a triangular region; in this way, six distinct triangular areas have been generated, where the nose tip is a common landmark to all the triangles. Next, assume a plane at the nose tip level for representing the volumetric space. The total density volume and some statistical features are considered for the experiment. From the volumetric space, construct 3D voxel representation.

Further, geometrical features from 3D voxel are used for the experiment. Three popular 3D face databases: Frav3D, Bosphorus, and Gavabdb are used as the input of the system. On these databases, the system acquires 94.28%, 95.3%, and 90.83% recognition rates using kNN and 95.59%, 96.37%, and 92.51% recognition rates using SVM classifier. Using geometric features with SVM classifier, the system acquires 92.09%, 93.67%, and 89.7% recognition rates.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abate, F., Nappi, M., Riccio, D., Sabatino, G.: 2D and 3D face recognition: a survey. Image Inf. Control 28(14), 1885–1906 (2007)

    Google Scholar 

  2. Gervei, O., Ayatollahi, A., Gervei, N.: 3D face recognition using modified PCA methods. World Acad. Sci. Eng. Technol. 4(39), 264 (2010)

    Google Scholar 

  3. Moreno, A.B., Sanchez, A., Velez, J.F., Diaz, J.: Face recognition using 3D local geometrical features: PCA vs. SVM. In: Proceedings of the ISPA, pp. 185–190 (2005)

    Google Scholar 

  4. Heseltine, T., Pears, N., Austin, J.: Three-dimensional face recognition: a fisher surface approach. In: Proceedings of the ICIAR, pp. 684–691 (2008)

    Google Scholar 

  5. Hesher, C., Srivastava, A., Erlebacher, G.: A novel technique for face recognition using range imaging. In: Proceedings of the Seventh International Symposium on Signal Processing and Its Applications, pp. 201–204 (2003)

    Google Scholar 

  6. Soltanpour, S., Boufama, B., Wu, Q.M.J.: A survey of local feature methods for 3D face recognition. Pattern Recognit. 72, 391–406 (2017)

    Article  Google Scholar 

  7. Dutta, K., Bhattacharjee, D., Nasipuri, M.: 3D face recognition based on volumetric representation of range image. In Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds.) Advance Computing and Systems for Security, Advance in Intelligent Systems and Computing, vol. 883, pp. 175–189 (2019)

    Google Scholar 

  8. Ganguly, S., Bhattacharjee, D., Nasipuri, M.: 3D face recognition from complement component range face images. In: IEEE International Conference on Computer Graphics, Vision and Information Security (CGVIS), pp. 275–278 (2015)

    Google Scholar 

  9. Moreno, A.B., Sanchez, A., Velez, J.F.: Voxel-based 3D face representations for recognition. In: 12th International Workshop on Systems, Signals and Image Processing, pp. 285–289 (2005)

    Google Scholar 

  10. Shekar, B.H., Harivinod, N., Kumara, M.S., Holla, K.R.: 3D face recognition using significant point based SULD descriptor. In: International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, Tamil Nadu, India, pp. 981–986 (2011)

    Google Scholar 

  11. Dutta, K., Bhattacharjee, D., Nasipuri, M.: Expression and occlusion invariant 3D face recognition based on region classifier. In: 1st International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 99–104. (2016)

    Google Scholar 

  12. Ganguly, S., Bhattacharjee, D., Nasipuri, M.: 2.5D face images: acquisition, processing and application. In: ICC 2014 - Computer Networks and Security, pp. 36–44 (2014)

    Google Scholar 

  13. Yin, L., Wang, R., Neuvo, Y.: Weighted median filters: a tutorial. IEEE Trans. Circuits Syst.-11: Analog. Digit. Signal Process. 43(3), 157–192 (1996)

    Google Scholar 

  14. Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. (T-PAMI) 14(2), pp. 239–256 (1992)

    Google Scholar 

  15. Ahdid, R., Taifi, K., Safi, S., Manaut, B.: A survey on facial features points detection techniques and approaches. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(8), 1566–1573 (2016)

    Google Scholar 

  16. Vezzetti, E., Marcolin, F.: Geometry-based 3D face morphology analysis: soft-tissue landmark formalization. Multimed. Tools Appl. 68(3), 895–929 (2014)

    Article  Google Scholar 

  17. Bagchi, P., Bhattacharjee, D., Nasipuri, M.: A robust analysis, detection and recognition of facial features in 2.5D images. Multimed. Tools Appl. 75(18), 11059–11096 (2016)

    Google Scholar 

  18. DDA line drawing. http://www.geeksforgeeks.org/dda-line-generation-algorithm-computer-graphics/

  19. Shin, D., Fowlkes, C.C., Hoiem, D.: Pixels, voxels, and views: a study of shape representations for single view 3D object shape prediction. CVPR (2018)

    Google Scholar 

  20. Vijayarekha, K.: Feature Extraction. https://nptel.ac.in/courses/117106100/Module%209/Lecture%204/LECTURE%204.pdf

  21. Santalo, L.A..: Integral Geometry and Geometric Probability. Cambridge Mathematical Library. Beijing World Publishing Corporation (BJWPC) (2004)

    Google Scholar 

  22. Mecke, K.R.: Additivity, convexity, and beyond: applications of Minkowski functionals in statistical physics. In: Mecke, K.R., Stoyan, D. (eds.) Statistical Physics and Spatial Statistics. LNP, vol. 554, pp. 111–184. Springer, Berlin (2000). https://doi.org/10.1007/3-540-45043-2_6

    Chapter  Google Scholar 

  23. Bosphorus. http://bosphorus.ee.boun.edu.tr/default.aspx

  24. FRAV3D. http://www.frav.es/databases

  25. GavabDB. http://gavab.escet.urjc.es/recursos_en.html

  26. Ganguly, S., Bhattacharjee, D., Nasipuri, M.: 3D face recognition from range images based on curvature analysis. ICTACT J. Image Video Process. 4(3), 748–753 (2014)

    Article  Google Scholar 

  27. Hajati, F. Gao, Y.: Pose-invariant 2.5D face recognition using geodesic texture warping. In: 11th International Conference on Control, Automation, Robotics and Vision Singapore, pp. 1837–1841 (2010)

    Google Scholar 

  28. Bagchi, P., Bhattacharjee, D., Nasipuri, M.: 3D face recognition using surface normals. In: TENCON 2015 - 2015 IEEE Region 10 Conference, pp. 1–6 (2015)

    Google Scholar 

  29. Naveen, S., Moni, R.S.: A robust novel method for face recognition from 2D depth images using DWT and DCT fusion. In: International Conference on Information and Communication Technologies (ICICT), Elsevier, pp. 1518–1528 (2014)

    Google Scholar 

  30. Dibeklioğlu, H., Gökberk, B., Akarun, L.: Nasal region-based 3D face recognition under pose and expression variations. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 309–318. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01793-3_32

    Chapter  Google Scholar 

  31. Alyuz, N., Gokberk, B., Akarun, L.: A 3D face recognition system for expression and occlusion invariance. In: BTAS 2008: Proceedings of the IEEE Second International Conference on Biometrics Theory, Applications and Systems, Arlington, Virginia, USA (2008)

    Google Scholar 

  32. Moreno, A.B., Sanchez, A., Velez, J.F., Diaz, J.: Face recognition using 3D surface-extracted descriptors. In: Irish Machine Vision and Image Processing Conference (2003)

    Google Scholar 

  33. Mousavi, M.H., Faez, K., Asghari, A.: Three dimensional face recognition using SVM classifier. In: Seventh IEEE/ACIS International Conference on Computer and Information Science, Portland, pp. 208–213 (2008)

    Google Scholar 

  34. Dutta, K., Bhattacharjee, D., Nasipuri, M.: TR-LBP: a modified local binary pattern-based technique for 3D face recognition. In: Fifth International Conference on Emerging Applications of Information Technology (EAIT) (2018)

    Google Scholar 

Download references

Acknowledgement

The first author is grateful to Ministry of Electronics and Information Technology (MeitY), Govt. of India for the grant of Visvesvaraya doctorate fellowship award. The authors are also thankful to CMATER laboratory of the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India for providing the necessary infrastructure for this work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, West Bengal, India

    Koushik Dutta, Debotosh Bhattacharjee & Mita Nasipuri

Authors
  1. Koushik Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debotosh Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mita Nasipuri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Koushik Dutta .

Editor information

Editors and Affiliations

  1. University of Calgary, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Sardina Systems OÜ, Tallinn, Estonia

    C. J. Kenneth Tan

  3. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, India

    Nabendu Chaki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer-Verlag GmbH Germany, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dutta, K., Bhattacharjee, D., Nasipuri, M. (2020). Volumetric Density of Triangulated Range Images for Face Recognition. In: Gavrilova, M., Tan, C., Saeed, K., Chaki, N. (eds) Transactions on Computational Science XXXV. Lecture Notes in Computer Science(), vol 11960. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61092-3_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-61092-3_4

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-61091-6

  • Online ISBN: 978-3-662-61092-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation