Expression Recognition Using the Periocular Region: A Feasibility Study

TL;DR

This study explores the potential of using the periocular region for facial expression recognition, demonstrating moderate accuracy with single-frame analysis, which is promising for scenarios with partial face visibility.

Contribution

It is the first to evaluate periocular-based expression recognition using multiple descriptors on a sizable dataset, highlighting its feasibility in unconstrained environments.

Findings

Achieved 67% accuracy with single-frame analysis.

Fusion of descriptors improved accuracy to 78%.

Periocular region offers a viable alternative when full face data is unavailable.

Abstract

This paper investigates the feasibility of using the periocular region for expression recognition. Most works have tried to solve this by analyzing the whole face. Periocular is the facial region in the immediate vicinity of the eye. It has the advantage of being available over a wide range of distances and under partial face occlusion, thus making it suitable for unconstrained or uncooperative scenarios. We evaluate five different image descriptors on a dataset of 1,574 images from 118 subjects. The experimental results show an average/overall accuracy of 67.0%/78.0% by fusion of several descriptors. While this accuracy is still behind that attained with full-face methods, it is noteworthy to mention that our initial approach employs only one frame to predict the expression, in contraposition to state of the art, exploiting several order more data comprising spatial-temporal data which is often not available.