We propose a view-constrained latent variable model for multi-view facial expression classification. In this model, we first learn a discriminative manifold shared by multiple views of facial expressions, followed by the expression classification in the shared manifold. For learning, we use the expression data from multiple views, however, the inference is performed using the data from a single view. Our experiments on data of posed and spontaneously displayed facial expressions show that the proposed approach outperforms the state-of-the-art methods for multi-view facial expression classification, and several state-of-the-art methods for multi-view learning.
CITATION STYLE
Eleftheriadis, S., Rudovic, O., & Pantic, M. (2014). View-constrained latent variable model for multi-view facial expression classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8888, pp. 292–303). Springer Verlag. https://doi.org/10.1007/978-3-319-14364-4_28
Mendeley helps you to discover research relevant for your work.