Greedy Search for Descriptive Spatial Face Features
This work addresses facial expression recognition for computer vision applications, but it is incremental as it focuses on feature selection within existing spatial feature methods.
The paper tackled the problem of facial expression recognition by selecting a descriptive subset of spatial features from a large pool using sequential forward selection, achieving 88.7% accuracy on the CK+ dataset without appearance-based features.
Facial expression recognition methods use a combination of geometric and appearance-based features. Spatial features are derived from displacements of facial landmarks, and carry geometric information. These features are either selected based on prior knowledge, or dimension-reduced from a large pool. In this study, we produce a large number of potential spatial features using two combinations of facial landmarks. Among these, we search for a descriptive subset of features using sequential forward selection. The chosen feature subset is used to classify facial expressions in the extended Cohn-Kanade dataset (CK+), and delivered 88.7% recognition accuracy without using any appearance-based features.