Spontaneous Facial Expression Recognition using Sparse Representation

TL;DR

This paper presents a novel approach for recognizing spontaneous facial expressions by learning discriminative dictionaries for sparse representation, improving robustness through a random face feature descriptor, and validating on multiple databases.

Contribution

It introduces a new dictionary learning method using a random face feature descriptor to enhance spontaneous facial expression recognition accuracy.

Findings

Effective recognition on DynEmo database

Comparable performance on JAFFE database

Improved robustness with random projection descriptor

Abstract

Facial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear combination of prototype atoms via Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an SVM classifier dedicated to the recognition task. The dictionary that sparsifies the facial images (feature points with the same class labels should have similar sparse codes) is crucial for robust classification. Learning sparsifying dictionaries heavily relies on the initialization process of the dictionary. To improve the performance of dictionaries, a random face feature descriptor based on the Random Projection concept is developed. The effectiveness of the proposed method is evaluated through several experiments on the spontaneous facial expressions DynEmo database. It is also estimated on the well-known acted facial expressions JAFFE database for a purpose of comparison with state-of-the-art methods.