Abstract

The same ob ject can be observed at different viewpoints or even by different sensors, thus generating multiple distinct even heteroge- neous samples. Nowadays, more and more applications need to recognize ob ject from distinct views. Some seminal works have been proposed for ob ject recognition across two views and applied to multiple views in some inefficient pairwise manner. In this paper, we propose a Multi-view Discriminant Analysis (MvDA) method, which seeks for a discriminant common space by jointly learning multiple view-specific linear trans- forms for robust ob ject recognition from multiple views, in a non-pairwise manner. Specifically, our MvDA is formulated to jointly solve the multi- ple linear transforms by optimizing a generalized Rayleigh quotient, i.e., maximizing the between-class variations and minimizing the within-class variations of the low-dimensional embeddings from both intra-view and inter-view in the common space. By reformulating this problem as a ra- tio trace problem, an analytical solution can be achieved by using the generalized eigenvalue decomposition. The proposed method is applied to three multi-view face recognition problems: face recognition across poses, photo-sketch face recognition, and Visual (VIS) image vs. Near Infrared (NIR) image face recognition. Evaluations are conducted respectively on Multi-PIE, CUFSF and HFB databases. Intensive experiments show that MvDA can achieve a more discriminant common space, with up to 13% improvement compared with the best known results.