Face Expression Recognition and Analysis: The State of the Art

TL;DR

This survey reviews the progress in automatic facial expression recognition since 2001, covering face detection, feature extraction, classification techniques, and future challenges, serving as a comprehensive tutorial for newcomers.

Contribution

It provides a detailed overview of the evolution, current state, and challenges in facial expression recognition, including databases, methods, and applications, with a tutorial approach.

Findings

Advances in face detection and tracking methods.

Development of standardized facial expression databases.

Progress in expression classification techniques.

Abstract

The automatic recognition of facial expressions has been an active research topic since the early nineties. There have been several advances in the past few years in terms of face detection and tracking, feature extraction mechanisms and the techniques used for expression classification. This paper surveys some of the published work since 2001 till date. The paper presents a time-line view of the advances made in this field, the applications of automatic face expression recognizers, the characteristics of an ideal system, the databases that have been used and the advances made in terms of their standardization and a detailed summary of the state of the art. The paper also discusses facial parameterization using FACS Action Units (AUs) and MPEG-4 Facial Animation Parameters (FAPs) and the recent advances in face detection, tracking and feature extraction methods. Notes have also been presented on emotions, expressions and facial features, discussion on the six prototypic expressions and the recent studies on expression classifiers. The paper ends with a note on the challenges and the future work. This paper has been written in a tutorial style with the intention of helping students and researchers who are new to this field.