Hierarchical Sparse and Collaborative Low-Rank Representation for Emotion Recognition

TL;DR

This paper introduces a novel hierarchical sparse and low-rank model for emotion recognition from visual data, effectively handling unavailability of explicit expression components and improving classification performance.

Contribution

The proposed C-HiSLR model combines low-rank and group sparsity to enhance emotion recognition without needing explicit expression components.

Findings

C-HiSLR performs comparably to SRC on raw expressive faces.

C-HiSLR achieves higher true positive rates than SRC.

Effective in recognizing emotions from unprocessed facial data.

Abstract

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.