Feature fusion for facial landmark detection

Panagiotis Perakis, Theoharis Theoharis, Ioannis A. Kakadiaris

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


Facial landmark detection is a crucial first step in facial analysis for biometrics and numerous other applications. However, it has proved to be a very challenging task due to the numerous sources of variation in 2D and 3D facial data. Although landmark detection based on descriptors of the 2D and 3D appearance of the face has been extensively studied, the fusion of such feature descriptors is a relatively under-studied issue. In this paper, a novel generalized framework for combining facial feature descriptors is presented, and several feature fusion schemes are proposed and evaluated. The proposed framework maps each feature into a similarity score and combines the individual similarity scores into a resultant score, used to select the optimal solution for a queried landmark. The evaluation of the proposed fusion schemes for facial landmark detection clearly indicates that a quadratic distance to similarity mapping in conjunction with a root mean square rule for similarity fusion achieves the best performance in accuracy, efficiency, robustness and monotonicity.

Original languageEnglish (US)
Pages (from-to)2783-2793
Number of pages11
JournalPattern Recognition
Issue number9
StatePublished - Sep 2014


  • Facial landmarks
  • Feature extraction
  • Feature fusion
  • Landmark detection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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