Micro-Expressions Detection Based on Micro-Motions Dense Optical Flows

Nistor, Sergiu Cosmin; Darabant, Adrian Sergiu; Borza, Diana

doi:10.23919/softcom.2018.8555833

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An apex frame is scored one if it is located between the onset and offset frames, and 0 otherwise. In another approach, Nistor et al [29] proposed a combination of metrics, i.e., intersection over union percentage, apex percentage, and intersection percentage, to determine the number of correct detected apex frames.…”

Section: Evaluation Protocolsmentioning

confidence: 99%

Sliding Window Based Micro-expression Spotting: A Benchmark

Tran

Hong

Zhao

2017

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

Abstract. Micro-expressions are very rapid and involuntary facial expressions, which indicate the suppressed or concealed emotions and can lead to many potential applications. Recently, research in micro-expression spotting obtains increasing attention. By investigating existing methods, we realize that evaluation standards of micro-expression spotting methods are highly desired. To address this issue, we construct a benchmark for fairer and better performance evaluation of micro-expression spotting approaches. Firstly, we propose a sliding window based multi-scale evaluation standard with a series of protocols. Secondly, baseline results of popular features are provided. Finally, we also raise the concerns of taking advantages of machine learning techniques.

show abstract

Section: Evaluation Protocolsmentioning

confidence: 99%

Sliding Window Based Micro-expression Spotting: A Benchmark

Tran

Hong

Zhao

2017

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

show abstract

Micro-expression spotting: A new benchmark

Tran

Hong

et al. 2021

Neurocomputing

View full text Add to dashboard Cite

Micro-Expression Spotting: A Benchmark

Hong¹,

Tran²,

Zhao³

2017

Preprint

View full text Add to dashboard Cite

Micro-expressions are rapid and involuntary facial expressions, which indicate the suppressed or concealed emotions. Recently, the research on automatic micro-expression (ME) spotting obtains increasing attention. ME spotting is a crucial step prior to further ME analysis tasks. The spotting results can be used as important cues to assist many other human oriented tasks and thus have many potential applications. In this paper, by investigating existing ME spotting methods, we recognize the immediacy of standardizing the performance evaluation of micro-expression spotting methods. To this end, we construct a micro-expression spotting benchmark (MESB). Firstly, we set up a sliding window based multi-scale evaluation framework. Secondly, we introduce a series of protocols. Thirdly, we also provide baseline results of popular methods. The MESB facilitates the research on ME spotting with fairer and more comprehensive evaluation and also enables to leverage the cutting-edge machine learning tools widely.This technical report is extended from an ACIVS17 paper. We are now expanding this work and updating this report when there are substantial achievements. The following citing information may be used for reference:'Thuong-Khanh Tran, Xiaopeng Hong, Guoying Zhao. Sliding-window based micro-expression spotting: A benchmark. In Proc. Advanced Concepts for Intelligent Vision Systems (ACIVS), 2017'.

show abstract

Micro-Expressions Detection Based on Micro-Motions Dense Optical Flows

Cited by 3 publications

References 13 publications

Sliding Window Based Micro-expression Spotting: A Benchmark

Sliding Window Based Micro-expression Spotting: A Benchmark

Micro-expression spotting: A new benchmark

Micro-Expression Spotting: A Benchmark

Contact Info

Product

Resources

About