Multi-level uncorrelated discriminative shared Gaussian process for multi-view facial expression recognition

Abstract

In multi-view facial expression recognition, discriminative shared Gaussian process latent variable model (DS-GPLVM) gives better performance than that of linear and nonlinear multi-view learning-based methods. However, Laplacian-based prior used in DS-GPLVM only captures topological structure of data space without considering the inter-class separability of the data, and hence the obtained latent space is suboptimal. So, we propose a multi-level uncorrelated DS-GPLVM (ML-UDSGPLVM) model which searches a common uncorrelated discriminative latent space learned from multiple observable spaces. A novel prior is proposed, which not only depends on the topological structure of the intra-class data, but also on the local-between-class-scatter-matrix of the data onto the latent manifold. The proposed approach employs an hierarchical framework, in which, expressions are first divided into three sub-categories. Subsequently, each of the sub-categories are further classified to identify the constituent basic expressions. Experimental results show that the proposed method outperforms state-of-the-art methods in many instances.