A Survey of the Trends in Facial and Expression Recognition Databases and Methods

TL;DR

This survey reviews the evolution of facial and expression recognition databases and methods, highlighting shifts from static image analysis to large-scale, dynamic data applications for security and identification.

Contribution

It provides a comprehensive overview of the development in datasets and methodologies, identifying trends and future directions in facial and expression recognition research.

Findings

Shift from static to dynamic image datasets

Recent focus on large-scale, internet-sourced data

Potential applications in security and personal identification

Abstract

Automated facial identification and facial expression recognition have been topics of active research over the past few decades. Facial and expression recognition find applications in human-computer interfaces, subject tracking, real-time security surveillance systems and social networking. Several holistic and geometric methods have been developed to identify faces and expressions using public and local facial image databases. In this work we present the evolution in facial image data sets and the methodologies for facial identification and recognition of expressions such as anger, sadness, happiness, disgust, fear and surprise. We observe that most of the earlier methods for facial and expression recognition aimed at improving the recognition rates for facial feature-based methods using static images. However, the recent methodologies have shifted focus towards robust implementation of facial/expression recognition from large image databases that vary with space (gathered from the internet) and time (video recordings). The evolution trends in databases and methodologies for facial and expression recognition can be useful for assessing the next-generation topics that may have applications in security systems or personal identification systems that involve "Quantitative face" assessments.