DIMENSIONALITY REDUCTION BASED ON LORENTZIAN MANIFOLD FOR FACE RECOGNITION


BİLGE H. Ş. , Kerimbekov Y., UĞURLU H. H.

10th International Conference on Electronics, Computer and Computation (ICECCO), Ankara, Türkiye, 7 - 09 Kasım 2013, ss.212-215 identifier identifier

  • Cilt numarası:
  • Doi Numarası: 10.1109/icecco.2013.6718266
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.212-215

Özet

Lorentzian geometry is a subject of mathematics and has famous applications in physics, especially in relativity theory. This geometry has interesting features, e. g. one axis has a negative sign in metric definition (time axis). In this study, we try to apply Lorentzian geometry for feature extraction and dimensionality reduction. We use a Lorentzian Manifold (LM) for face recognition and reduce the dimensionality in this new feature space. We compare results with different feature extraction methods; Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP). Our experiments show that the best feature extraction method is LM and it produces the best face recognition rates. It is also powerful in dimensionality reduction.