Less is More: Micro-expression Recognition from Video using Apex Frame

TL;DR

This paper introduces a micro-expression recognition method using only the apex and onset frames, employing a new feature extractor, Bi-WOOF, which achieves state-of-the-art results on multiple datasets.

Contribution

The study proposes a novel approach that simplifies micro-expression recognition by using only two key frames and a new feature extraction method, improving accuracy.

Findings

Achieved 61% F1-score on CASME II

Achieved 62% F1-score on SMIC-HS

Outperformed existing methods on five datasets

Abstract

Despite recent interest and advances in facial micro-expression research, there is still plenty room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100-200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video: the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases: CAS(ME)$^2$, CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 61% and 62% in the high frame rate CASME II and SMIC-HS databases respectively.