Face recognition using Laplacianfaces

TLDR

Face recognition traditionally relies on PCA or LDA, which capture only Euclidean structure, whereas LPP preserves local manifold information and can be derived from different graph models. The authors propose the Laplacianface method, an appearance‑based face recognition approach. The method maps face images into a subspace via LPP, approximating Laplace‑Beltrami eigenfunctions, and is evaluated against Eigenface and Fisherface on three datasets. The approach reduces variations from lighting, expression, and pose, and experimental results show lower error rates than Eigenface and Fisherface.

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.