Micro-expression spotting: A new benchmark

Tran, Thuong-Khanh; Vo, Quang-Nhat; Xiao, Hong; Li, Xiaobai; Zhao, Guoying

doi:10.48550/arxiv.2007.12421

Cited by 1 publication

(1 citation statement)

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“…Table 3 compares the state-of-the-art micro-expression spotting methods. Several earlier studies on automatic facial ME analysis primarily focused on distinguishing facial micro-expressions from macro-expressions [135], [136], [137]. Shreve et al [136], [137] used an optical flow method for automatic ME spotting on their own database.…”

Section: Micro-expression Spottingmentioning

confidence: 99%

An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challenge

Xie¹,

Lo²,

Shuai³

et al. 2020

Preprint

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Facial micro-expressions indicate brief and subtle facial movements that appear during emotional communication. In comparison to macro-expressions, micro-expressions are more challenging to be analyzed due to the short span of time and the fine-grained changes. In recent years, micro-expression recognition (MER) has drawn much attention because it can benefit a wide range of applications, e.g. police interrogation, clinical diagnosis, depression analysis, and business negotiation. In this survey, we offer a fresh overview to discuss new research directions and challenges these days for MER tasks. For example, we review MER approaches from three novel aspects: macro-to-micro adaptation, recognition based on key apex frames, and recognition based on facial action units. Moreover, to mitigate the problem of limited and biased ME data, synthetic data generation is surveyed for the diversity enrichment of micro-expression data. Since micro-expression spotting can boost micro-expression analysis, the state-of-the-art spotting works are also introduced in this paper. At last, we discuss the challenges in MER research and provide potential solutions as well as possible directions for further investigation.

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