  In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank
(C-HiSLR) model that is natural for recognizing human emotion in visual data.
Previous attempts require explicit expression components, which are often
unavailable and difficult to recover. Instead, our model exploits the lowrank
property over expressive facial frames and rescue inexact sparse
representations by incorporating group sparsity. For the CK+ dataset, C-HiSLR
on raw expressive faces performs as competitive as the Sparse Representation
based Classification (SRC) applied on manually prepared emotions. C-HiSLR
performs even better than SRC in terms of true positive rate.
