Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces

  • Authors:

  • Abhishek Sharma;Anamika Dubey;Pushkar Tripathi;Vinod Kumar

  • Affiliations:

  • Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667, Uttarakhand, India;Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667, Uttarakhand, India;Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667, Uttarakhand, India;Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667, Uttarakhand, India

  • Venue:
  • Neurocomputing
  • Year:
  • 2010

Quantified Score

Hi-index 0.01

Visualization

Abstract

A novel view-based subspace termed as hybrid-eigenspace is introduced and used to synthesize multiple virtual views of a person under different pose and illumination from a single 2D image. The synthesized virtual views are used as training samples in some subspace classifiers (LDA (Belhumeur et al., 1997) [4], 2D LDA (Kong et al., 2005) [22], 2D CLAFIC (Cevikalp et al., 2009) [23], 2D CLAFIC-@m (Cevikalp et al., 2009) [23], NFL (Pang et al., 2007) [18] and ONFL (Pang et al., 2009) [19]) requiring multiple training image for pose and illumination invariant face recognition. The complete process is termed as virtual classifier and provides efficient solution to the ''single sample problem'' of aforementioned classifiers. The presented work extends the eigenfaces by introducing hybrid-eigenfaces which are different from the view-based eigenfaces originally proposed by Turk and Pentland (1994) [37]. Hybrid-eigenfaces exhibit properties that are common to faces and eigenfaces. Existence of high correlation between the corresponding hybrid-eigenfaces under different poses (absent in eigenfaces) is one such property. It allows efficient fusion of hybrid-eigenfaces with global linear regression (GLR) (Chai et al., 2007) [36] to synthesize virtual multi-view images which does not require pixel-wise dense correspondence and all the processes are strictly restricted to 2D domain which saves a lot of memory and computation resources. Effectively, PCA and aforementioned subspaces are extended by the presented work and used for more robust face recognition from single training image. Proposed methodology is extensively tested on two databases (FERET and Yale) and the results exhibited significant improvement in terms of tolerance to pose difference and illumination variation between gallery and test images over other 2D methods.