FLD-SIFT: Class Based Scale Invariant Feature Transform for Accurate Classification of Faces

  • B. H. Shekar
  • M. Sharmila Kumari
  • Leonid M. Mestetskiy
  • Natalia Dyshkant
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 142)


In this paper, a new model called FLD-SIFT is devised for compact representation and accurate recognition of faces. Unlike scale invariant feature transform model that uses smoothed weighted histogram and massive dimension of feature vectors, in the proposed model, an image patch centered around the keypoint has been considered and linear discriminant analysis (FLD) is employed for compact representation of image patches. Contrasting to PCA-SIFT model that employs principal component analysis (PCA) on a normalized gradient patch, we employ FLD on an image patch exists around the keypoints. The proposed model has better computing performance in terms of recognition time than the basic SIFT model. To establish the superiority of the proposed model, we have experimentally compared the performance of our new algorithm with (2D)2-PCA, (2D)2-FLD and basic SIFT model on the AT&T face database.


Linear discriminant analysis Local descriptor Face classification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • B. H. Shekar
    • 1
  • M. Sharmila Kumari
    • 2
  • Leonid M. Mestetskiy
    • 3
  • Natalia Dyshkant
    • 3
  1. 1.Department of Computer ScienceMangalore UniversityIndia
  2. 2.Department of Computer Science and EngineeringP A College of EngineeringMangaloreIndia
  3. 3.Department of Computational Mathematics and CyberneticsMoscow State UniversityMoscowRussia

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