Dimensionality reduction based on Lorentzian Manifold for face recognition

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 extracti...

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Bibliographic Details
Main Authors: Bilge, Hasan Sakir, Kerimbekov, Yerzhan, Ugurlu, Hasan Huseyin
Format: Conference Proceeding
Language:English
Subjects:
classification
dimensionality reduction
Face
Face recognition
Feature extraction
Lorentzian manifold
Manifolds
Measurement
Principal component analysis
Online Access:Request full text
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Summary: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.
DOI:10.1109/ICECCO.2013.6718266