Spontaneous Subtle Expression Detection and Recognition based on Facial Strain

TL;DR

This paper introduces a novel method for detecting and recognizing micro-expressions using facial optical strain features, outperforming baseline methods on CASME II and SMIC databases.

Contribution

The paper presents a new approach utilizing optical strain magnitudes for micro-expression detection and recognition, demonstrating improved accuracy over existing methods.

Findings

Optical strain features enhance micro-expression detection.

The proposed method outperforms baseline results.

Comparison shows advantages over other spatio-temporal features.

Abstract

Optical strain is an extension of optical flow that is capable of quantifying subtle changes on faces and representing the minute facial motion intensities at the pixel level. This is computationally essential for the relatively new field of spontaneous micro-expression, where subtle expressions can be technically challenging to pinpoint. In this paper, we present a novel method for detecting and recognizing micro-expressions by utilizing facial optical strain magnitudes to construct optical strain features and optical strain weighted features. The two sets of features are then concatenated to form the resultant feature histogram. Experiments were performed on the CASME II and SMIC databases. We demonstrate on both databases, the usefulness of optical strain information and more importantly, that our best approaches are able to outperform the original baseline results for both detection and recognition tasks. A comparison of the proposed method with other existing spatio-temporal feature extraction approaches is also presented.