Geometric Preserving Local Fisher Discriminant Analysis for person re-identification

• A novel metric learning method is proposed for person re-identification.
• A novel assumption that the re-id data lies on a nonlinear manifold is made.
• Geometric structure is incorporated with nearest neighbor graph.
• The problem is solved effectively without complex iteration.
• Kernel extension of the method is proposed.

Recently, Local Fisher Discriminant Analysis (LFDA) has achieved impressive performance in person re-identification. However, the classic LFDA method pays little attention to the intrinsic geometrical structure of the complex person re-identification data. Due to large appearance variance, two images of the same person may be far away from each other in feature space while images of different people may be quite close to each other. The linear topology exploited in LFDA is not sufficient to describe this nonlinear data structure. In this paper, we assume that the data reside on a manifold and propose an effective method termed Geometric Preserving Local Fisher Discriminant Analysis (GeoPLFDA). The method integrates discriminative framework of LFDA with geometric preserving method which approximates local manifold utilizing a nearest neighbor graph. LFDA provides discriminative information by separating different labeled samples and pulling the same labeled samples together. The geometric preserving projection provides local manifold structure of the nonlinear data induced by graph topology. Taking advantage of the complementary between them, the proposed method achieves significant improvement over state-of-the-art approaches. Furthermore, a kernel extension of the GeoPLFDA method is proposed to handle the complex nonlinearity more effectively and to further improve re-identification accuracy. Experiments on the challenging iLIDS, VIPeR, CAVIAR and 3DPeS datasets demonstrate the effectiveness of the proposed method.