Advertisement

Experimental Evaluation of 3D Kinect Face Database

  • A. A. GaonkarEmail author
  • M. D. Gad
  • N. T. Vetrekar
  • Vithal Shet Tilve
  • R. S. Gad
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10481)

Abstract

3D face recognition has gain a paramount importance over 2D due to its potential to address the limitations of 2D face recognition against the variation in facial poses, angles, occlusions etc. Research in 3D face recognition has accelerated in recent years due to the development of low cost 3D Kinect camera sensor. This has leads to the development of few RGB-D database across the world. Here in this paper we introduce the base results of our 3D facial database (GU-RGBD database) comprising variation in pose (0°, 45°, 90°, −45°, −90°), expression (smile, eyes closed), occlusion (half face covered with paper) and illumination variation using Kinect. We present a proposed noise removal non-linear interpolation filter for the patches present in the depth images. The results were obtained on three face recognition algorithms and fusion at matching score level for recognition and verification rate. The obtained results indicated that the performance with our proposed filter shows improvement over pose with score level fusion using sum rule.

Notes

Acknowledgment

Authors would like to acknowledge the financial assistance from Minister of Electronics and Information Technology (MeitY) under Visvesvaraya PhD Scheme for carrying out research work at Goa University. Authors are also thankful to Ms. Bhagyada Pai Kane, Ms. Shweta Sawal Desai and Mr. Saurabh Vernekar (Post graduate students, Department of Electronics, 2015 batch) for their support in the RGBD database collection and to all the subjects for their valuable participation.

References

  1. 1.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face recognition - a literature survey. ACM Comput. Surv. 35(44), 399–458 (2003)CrossRefGoogle Scholar
  2. 2.
    Sharma, P.B., Goyani, M.M.: 3D face recognition techniques - a review. Int. J. Eng. Res. Appl. (IJERA) 2(1), 787–793 (2012)Google Scholar
  3. 3.
    Li, B.Y.L., Mian, A.S., Liu, W., Krishna, A.: Using Kinect for face recognition under varying poses, expressions, illumination and disguise. In: Applications of Computer Vision (WACV) IEEE Workshop, pp. 15–17 (2013)Google Scholar
  4. 4.
    Goswami, G., Vatsa, M., Singh, R.: RGB-D face recognition with texture and attribute features. IEEE Trans. Inf. Forensics Secur. 9, 1629–1640 (2014)CrossRefGoogle Scholar
  5. 5.
    Ekenel, H.K., Gao, H., Stiefelhagen, R.: 3-D face recognition using local appearance-based models. IEEE Trans. Inf. Forensics Secur. 2(3), 630–636 (2007)CrossRefGoogle Scholar
  6. 6.
    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: ACCV 2012, Workshop on Computer Vision with Local Binary Pattern Variants, Daejeon, Korea, pp. 5–9 (2012)Google Scholar
  7. 7.
    Vezzetti, E., Marcolin, F., Fracastoro, G.: 3D face recognition - an automatic strategy based on geometrical descriptors and landmarks. Robot. Auton. Syst. 62, 1768–1776 (2014)CrossRefGoogle Scholar
  8. 8.
    Min, R., Kose, N., Dugelay, J.-L.: KinectFaceDB - a Kinect database for face recognition. IEEE Trans. Syst. Man Cybern.: Syst. 44, 1534–1548 (2014)CrossRefGoogle Scholar
  9. 9.
    Ajmera, R., Nigam, A., Gupta, P.: 3D face recognition using Kinect. In: ICVGIP 2014, Bangalore, India, pp. 14–18 (2014)Google Scholar
  10. 10.
    Hg, R.I., 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: IEEE 8th International Conference on Signal Image Technology and Internet Based Systems (2012)Google Scholar
  11. 11.
    Goswami, G., Bharadwaj, S., Vatsa, M., Singh, R.: On RGB-D face recognition using Kinect. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS) (2013)Google Scholar
  12. 12.
    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)CrossRefzbMATHGoogle Scholar
  13. 13.
    Mao, Y., Cheung, G., Ortega, A., Ji, Y.: Expansion hole filling in depth-image-based rendering using graph-based interpolation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 26–31 (2013)Google Scholar
  14. 14.
    Solh, M., AlRegib, G.: Hierarchical hole-filling for depth-based view synthesis in FTV and 3D video. IEEE J. Sel. Top. Sig. Process. 6(5), 495–504 (2012)CrossRefGoogle Scholar
  15. 15.
    Wang, D., Zhao, Y., Wang, J., Wang, Z.: A hole filling algorithm for depth image based rendering based on gradient information. In: 2013 Ninth International Conference on Natural Computation (ICNC) (2013)Google Scholar
  16. 16.
    Wang, D., Zhao, Y., Wang, Z., Chen, H.: Hole-filling for DIBR based on depth and gradient information. Int. J. Adv. Robot. Syst. 12(2) (2015)Google Scholar
  17. 17.
    Feng, L., Po, L.-M., Xu, X., Ng, K.-H., Cheung, C.-H., Cheung, K.-W.: An adaptive background biased depth map hole-filling method for Kinect. In: Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE (2013)Google Scholar
  18. 18.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) (2005)Google Scholar
  19. 19.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognit. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  20. 20.
    Aly, S., Abbott, L., White, S., Youssef, A.: VT-KFER - a Kinect-based RGBD+ time dataset for spontaneous and non-spontaneous facial expression recognition. In: 2015 International Conference on Biometrics (ICB) (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • A. A. Gaonkar
    • 1
    Email author
  • M. D. Gad
    • 2
  • N. T. Vetrekar
    • 1
  • Vithal Shet Tilve
    • 3
  • R. S. Gad
    • 1
  1. 1.Department of ElectronicsGoa UniversityTaleigao PlateauIndia
  2. 2.Goa Engineering CollegeFarmagudiIndia
  3. 3.School of Earth and Space Exploration Arizona State UniversityTempeUSA

Personalised recommendations