Facial Expression Recognition Using Sparse Gaussian Conditional Random Field

TL;DR

This paper introduces a novel facial expression recognition model based on Sparse Gaussian Conditional Random Fields, demonstrating superior performance over existing methods through experiments on CK+ and RU-FACS datasets.

Contribution

The paper proposes a new Gaussian Conditional Random Field model for facial expression recognition, optimized with ADMM, and shows improved accuracy over state-of-the-art methods.

Findings

Outperforms existing expression recognition methods

Effective on CK+ and RU-FACS datasets

Demonstrates robustness and accuracy

Abstract

The analysis of expression and facial Action Units (AUs) detection are very important tasks in fields of computer vision and Human Computer Interaction (HCI) due to the wide range of applications in human life. Many works has been done during the past few years which has their own advantages and disadvantages. In this work we present a new model based on Gaussian Conditional Random Field. We solve our objective problem using ADMM and we show how well the proposed model works. We train and test our work on two facial expression datasets, CK+ and RU-FACS. Experimental evaluation shows that our proposed approach outperform state of the art expression recognition.