Advertisement

The Visual Computer

, Volume 30, Issue 11, pp 1275–1292 | Cite as

Selecting stable keypoints and local descriptors for person identification using 3D face scans

  • Stefano Berretti
  • Naoufel Werghi
  • Alberto del Bimbo
  • Pietro Pala
Original Article

Abstract

3D face identification based on the detection and comparison of keypoints of the face is a promising solution to extend face recognition approaches to the case of 3D scans with occlusions and missing parts. In fact, approaches that perform sparse keypoints matching can naturally allow for partial face comparison. However, such methods typically use a large number of keypoints, locally described by high-dimensional feature vectors: This, combined with the combinatorial number of keypoint comparisons required to match two face scans, results in a high computational cost that does not scale well with large datasets. Motivated by these considerations, in this paper, we present a 3D face recognition approach based on the meshDOG keypoints detector and local GH descriptor, and propose original solutions to improve keypoints stability and select the most effective features from the local descriptors. Experiments have been performed to assess the validity of the proposed optimizations for stable keypoints detection and feature selection. Recognition accuracy has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face identification solutions based on 3D keypoints.

Keywords

3D face recognition 3D Keypoints detection Stable scale space selection Feature selection 

References

  1. 1.
    Al-Osaimi, F.R., Bennamoun, M., Mian, A.: An expression deformation approach to non-rigid 3D face recognition. Int. J. Comput. Vis. 81(3), 302–316 (2009)CrossRefGoogle Scholar
  2. 2.
    Ashbrook, A., Fisher, R., Robertson, C., Werghi, N.: Finding surface correspondance for object recognition and registration using pairwise geometric histograms. In: Proceedings of European Conference on Computer Vision, pp. 674–686. Friburg (1998)Google Scholar
  3. 3.
    Berretti, S., Del Bimbo, A., Pala, P.: 3D face recognition using iso-geodesic stripes. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2162–2177 (2010)Google Scholar
  4. 4.
    Berretti, S., Del Bimbo, A., Pala, P.: Superfaces: A super-resolution model for 3D faces. In: Proceedings Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment. Firenze (2012)Google Scholar
  5. 5.
    Berretti, S., Del Bimbo, A., Pala, P.: Sparse matching of salient facial curves for recognition of 3D faces with missing parts. IEEE Trans. Inf. Forensics Secur. 8(2), 374–389 (2013)Google Scholar
  6. 6.
    Berretti, S., Werghi, N., Del Bimbo, A., Pala, P.: Matching 3D face scans using interest points and local histogram descriptors. Comput. Graph. 37(6), 509–525 (2013)CrossRefGoogle Scholar
  7. 7.
    Bowyer, K.W., Chang, K.I., Flynn, P.J.: A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput. Vis. Image Underst. 101(1), 1–15 (2006)CrossRefGoogle Scholar
  8. 8.
    Boyer, E., Bronstein, A.M., Bronstein, M.M., Bustos, B., Darom, T., Horaud, R., Hotz, I., Keller, Y., Keustermans, J., Kovnatsky, A., Litman, R., Reininghaus, J., Sipiran, I., Smeets, D., Suetens, P., Vandermeulen, D., Zaharescu, A., Zobel, V.: SHREC 2011: Robust feature detection and description benchmark. In: Proceedings of Eurographics Workshop on 3D Object Retrieval (3DOR 2011). Llandudno (2011)Google Scholar
  9. 9.
    De Carlo, D., Metaxas, D., Stone, M.: An anthropometric face model using variational techniques. In: Proceedings of ACM SIGGRAPH, pp. 67–74. Orlando (1998)Google Scholar
  10. 10.
    Dorkó, G., Schmid, C.: Maximally stable local description for scale selection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) Computer Vision ECCV 2006. Lecture Notes in Computer Science, vol. 3954, pp. 504–516. Springer, Berlin (2006)Google Scholar
  11. 11.
    Drira, H., Amor, B.B., Srivastava, A., Daoudi, M., Slama, R.: 3D face recognition under expressions, occlusions and pose variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2270–2283 (2013)Google Scholar
  12. 12.
    Erdogmus, N., Marcel, S.: Spoofing in 2D face recognition with 3D masks and anti-spoofing with kinect. In: Proceedings of IEEE International Conference on Biometrics: Theory, Applications and Systems. Washington DC (2013)Google Scholar
  13. 13.
    Faltemier, T.C., Bowyer, K.W., Flynn, P.J.: A region ensemble for 3D face recognition. IEEE Trans. Inf. Forensics Secur. 3(1), 62–73 (2008)Google Scholar
  14. 14.
    Farkas, L.G.: Anthropometry of the Head and Face. Raven Press, New York (1994)Google Scholar
  15. 15.
    Goswami, G., Bharadwaj, S., Vatsa, M., Singh, R.: On RGB-D face recognition using Kinect. In: Proceedings of IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS). Washington DC (2013)Google Scholar
  16. 16.
    Huang, D., Ardabilian, M., Wang, Y., Chen, L.: 3D face recognition using eLBP-based facial representation and local feature hybrid matching. IEEE Trans. Inf. Forensics Secur. 7(5), 1551–1565 (2012)CrossRefGoogle Scholar
  17. 17.
    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: Proceedings of ACCV Workshop on Computer Vision with Local Binary Pattern Variants. Daejeon (2012)Google Scholar
  18. 18.
    Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, N., Lu, Y., Karampatziakis, N., Theoharis, T.: Three-dimensional face recognition in the presence of facial expressions: an annotated deformable approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 640–649 (2007)CrossRefGoogle Scholar
  19. 19.
    Lei, Y., Bennamoun, M., Guo, M.H.Y.: An efficient 3D face recognition approach using local geometrical signatures. Pattern Recognit. 47(2), 509–524 (2014)CrossRefGoogle Scholar
  20. 20.
    Li, B.Y.L., Mian, A.S., Liu, W., Krishna, A.: Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 186–192. Clearwater (2013)Google Scholar
  21. 21.
    Li, H., Huang, D., Lemaire, P., Morvan, J.M., Chen, L.: Expression robust 3D face recognition via mesh-based histograms of multiple order surface differential quantities. In: Proceedings of IEEE International Conference on Image Processing, pp. 3053–3056 (2011)Google Scholar
  22. 22.
    Lowe, D.: Distinctive image features from scale-invariant key points. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  23. 23.
    Maes, C., Fabry, T., Keustermans, J., Smeets, D., Suetens, P., Vandermeulen, D.: Feature detection on 3D face surfaces for pose normalisation and recognition. In: IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–6. Washington DC (2010)Google Scholar
  24. 24.
    Mian, A.S., Bennamoun, M., Owens, R.: Keypoint detection and local feature matching for textured 3D face recognition. Int. J. Comput. Vis. 79(1), 1–12 (2008)CrossRefGoogle Scholar
  25. 25.
    Min, R., Choi, J., Medioni, G., Dugelay, J.L.: Real-time 3D face identification from a depth camera. In: Proceedings of International Conference on Pattern Recognition, pp. 1739–1742. Tsukuba (2012)Google Scholar
  26. 26.
    Passalis, G., Perakis, P., Theoharis, T., Kakadiaris, I.A.: Using facial symmetry to handle pose variations in real-world 3D face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1938–1951 (2011)CrossRefGoogle Scholar
  27. 27.
    Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency max-relevance and min-redundancy. EEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)CrossRefGoogle Scholar
  28. 28.
    Perakis, P., Passalis, G., Theoharis, T., Kakadiaris, I.A.: 3D facial landmark detection under large yaw and expression variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(7), 1552–1564 (2013)CrossRefGoogle Scholar
  29. 29.
    Peyre, G.: Toolbox graph. In: MATLAB Central File Exchange Select (2009)Google Scholar
  30. 30.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proceedings of IEEE Workshop on Face Recognition Grand Challenge Experiments, pp. 947–954. San Diego (2005)Google Scholar
  31. 31.
    Savran, A., Alyüz, N., Dibeklioǧlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., Akarun, L.: Bosphorus database for 3D face analysis. In: Proceedings of COST 2101 Workshop on Biometrics and Identity Management (2008)Google Scholar
  32. 32.
    Smeets, D., Keustermans, J., Vandermeulen, D., Suetens, P.: meshSIFT: Local surface features for 3D face recognition under expression variations and partial data. Comput. Vis. Image Underst. 117(2), 158–169 (2013)Google Scholar
  33. 33.
    Tombari, F., Salti, S., Di Stefano, L.: Performance evaluation of 3D keypoint detectors. Int. J. Comput. Vis. 102(2–3), 198–220 (2013)CrossRefGoogle Scholar
  34. 34.
    Wang, Y., Liu, J., Tang, X.: Robust 3D face recognition by local shape difference boosting. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 1858–1870 (2010)CrossRefGoogle Scholar
  35. 35.
    Werghi, N., Rahayem, M., Kjellander, J.: An ordered topological representation of 3D triangular mesh facial surface: concept and applications. EURASIP J. Adv. Signal Process. 2012(144), 1–20 (2012)Google Scholar
  36. 36.
    Yin, L., Wei, X., Sun, Y., Wang, J., Rosato, M.: A 3D facial expression database for facial behavior research. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 211–216. Southampton (2006)Google Scholar
  37. 37.
    Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.: Surface feature detection and description with applications to mesh matching. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 373–380. Miami Beach (2009)Google Scholar
  38. 38.
    Zuliani, M., Kenney, C.S., Manjunath, B.S.: The multiransac algorithm and its application to detect planar homographies. In: Proceedings of IEEE International Conference on Image Processing, pp. 153–156. Genoa (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stefano Berretti
    • 1
  • Naoufel Werghi
    • 2
  • Alberto del Bimbo
    • 1
  • Pietro Pala
    • 1
  1. 1.Department of Information EngineeringUniversity of FlorenceFlorenceItaly
  2. 2.Department of Electrical and Computer EngineeringKhalifa University of Science, Technology and ResearchAbu DhabiUnited Arab Emirates

Personalised recommendations