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


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.


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


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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

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