Face Recognition Using Global and Local Salient Features

  • Dakshina Ranjan Kisku
  • Phalguni Gupta
  • Jamuna Kanta Sing
  • Massimo Tistarelli
Part of the Computer Communications and Networks book series (CCN)


This chapter presents a robust face recognition technique which is based on the extraction of Scale Invariant Feature Transform (SIFT) features from the face areas. It uses both a global and local matching strategy. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. The Dempster–Shafer decision theory is applied to fuse the two matching strategies. The proposed technique has been evaluated with the Indian Institute of Technology Kanpur (IITK), Olivetti Research Laboratory (ORL) (formerly known as AT&T face database), and the Yale face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in cases of partially occluded faces or with missing information. Besides this, some state-of-the-art face recognition techniques have been presented and the current face-matching technique is compared with those techniques while all the matching techniques use SIFT descriptors as local features.


Face Image Scale Invariant Feature Transform Face Database Equal Error Rate Basic Probability Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Dakshina Ranjan Kisku
    • 1
  • Phalguni Gupta
    • 2
  • Jamuna Kanta Sing
    • 3
  • Massimo Tistarelli
    • 4
  1. 1.Department of Computer Science and EngineeringDr. B. C. Roy Engineering CollegeDurgapurIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology KanpurKanpurIndia
  3. 3.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  4. 4.Computer Vision Lab, DAPUniversity of SassariAlgheroItaly

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