DISTRIBUTED COMPUTING FACE RECOGNITION USING LAPLACIAN FACES

DISTRIBUTED COMPUTING FACE RECOGNITION USING LAPLACIAN FACES

Abstract—We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality
Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component
Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an
embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The
Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In
this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced.
Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed
Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that
the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition

DISTRIBUTED COMPUTING FACE RECOGNITION USING LAPLACIAN FACES