Sparse Coding Trees with application to emotion classification

We present Sparse Coding trees (SC-trees), a sparse coding-based framework for resolving misclassifications arising when multiple classes map to a common set of features. SC-trees are novel supervised classification trees that use node-specific dictionaries and classifiers to direct input based on classification results in the feature space at each node. We have applied SC-trees to emotion classification of facial expressions. This paper uses this application to illustrate concepts of SC-trees and how they can achieve high performance in classification tasks. When used in conjunction with a nonnegativity constraint on the sparse codes and a method to exploit facial symmetry, SC-trees achieve results comparable with or exceeding the state-of-the-art classification performance on a number of realistic and standard datasets.