Multi-pose Face Recognition Using Fusion of Scale Invariant Features

  • I Gede Pasek Suta Wijaya
  • Keiichi Uchimura
  • Gou Koutaki
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 144)

Abstract

This paper presents a new multi-pose face recognition approach using fusion of scale invariant features (FSIF). The FSIF is a face descriptor representing 3D face images features which is created by fusing some scale invariant features extracted by scale invariant features transforms (SIFT) from several different poses of 2D face images. The main aim of this method is to avoid using 3D scanner for estimating any pose variations of a face image but it still have reasonable achievement compare to 3D-based face recognition method for multi-pose face recognition. The experimental results show the proposed method is sufficiently to overcame large face variability due to face pose variations.

Keywords

Face Recognition Linear Discriminant Analysis Recognition Rate Face Image Scale Invariant Feature Transform 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • I Gede Pasek Suta Wijaya
    • 1
    • 2
  • Keiichi Uchimura
    • 2
  • Gou Koutaki
    • 2
  1. 1.Electrical Engineering DepartmentMataram UniversityMataramIndonesia
  2. 2.Computer Science and Electrical Engineering Department GSSTKumamoto UniversityKumamotoJapan

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