CAS(ME)&lt;sup&gt;3&lt;/sup&gt;: A Third Generation Facial Spontaneous Micro-Expression Database with Depth Information and High Ecological Validity

Li, Jingting; Dong, Zizhao; Lu, Shaoyuan; Wang, Sujing; Yan, Wen-Jing; Ma, Yinhuan; Liu, Ye; Huang, Chang-Bing; Fu, Xiaolan

doi:10.1109/tpami.2022.3174895

Cited by 47 publications

(32 citation statements)

References 80 publications

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“…That is, since the oxytocin system may suppress the processing of signals that are related to deception to facilitate social adaptation ( Ma et al, 2016 ; Pfundmair et al, 2017 ) and leaked surprise and disgust are more closely related to deception and other hostile intentions than the leaked emotion of sadness and happiness (e.g., Ten Brinke et al, 2012 ; Ten Brinke and Porter, 2012 ), it is possible for oxytocin to have a much more stronger inhibiting effect on the recognition of micro-expressions of surprise and disgust than the micro-expressions of sadness and happiness. To test external validity of the current results, researchers still need to further investigate the effects of oxytocin on the recognition of micro-expressions by employing more sufficient trainings and on other natural micro-expression datasets as well (e.g., the CAS(ME) 3 natural micro-expression database; Li et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Oxytocin Impairs the Recognition of Micro-Expressions of Surprise and Disgust

Xie

Liu

et al. 2022

Front. Psychol.

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As fleeting facial expressions which reveal the emotion that a person tries to conceal, micro-expressions have great application potentials for fields like security, national defense and medical treatment. However, the physiological basis for the recognition of these facial expressions is poorly understood. In the present research, we utilized a double-blind, placebo-controlled, mixed-model experimental design to investigate the effects of oxytocin on the recognition of micro-expressions in three behavioral studies. Specifically, in Studies 1 and 2, participants were asked to perform a laboratory-based standardized micro-expression recognition task after self-administration of a single dose of intranasal oxytocin (40 IU) or placebo (containing all ingredients except for the neuropeptide). In Study 3, we further examined the effects of oxytocin on the recognition of natural micro-expressions. The results showed that intranasal oxytocin decreased the recognition speed for standardized intense micro-expressions of surprise (Study 1) and decreased the recognition accuracy for standardized subtle micro-expressions of disgust (Study 2). The results of Study 3 further revealed that intranasal oxytocin administration significantly reduced the recognition accuracy for natural micro-expressions of surprise and disgust. The present research is the first to investigate the effects of oxytocin on micro-expression recognition. It suggests that the oxytocin mainly plays an inhibiting role in the recognition of micro-expressions and there are fundamental differences in the neurophysiological basis for the recognition of micro-expressions and macro-expressions.

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Section: Discussionmentioning

confidence: 99%

Oxytocin Impairs the Recognition of Micro-Expressions of Surprise and Disgust

Xie

Liu

et al. 2022

Front. Psychol.

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“…Deep learning is a data-driven method, and successful training requires various large-scale data. Recent studies indicated that annotation bias, emotional contexts, and cultural backgrounds could affect the ME perception [195]. They may mislead the model training and finally cause misclassification.…”

Section: Dealing With Small-scale Datasetmentioning

confidence: 99%

Deep Learning for Micro-Expression Recognition: A Survey

Wei

Liu

et al. 2022

IEEE Trans. Affective Comput.

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Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in various fields. Early methods for Micro-expression Recognition (MER) are mainly based on traditional features. Recently, with the success of Deep Learning (DL) in various tasks, neural networks have received increasing interest in MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid facial movements, leading to difficult data collection and annotation, thus publicly available datasets are usually small-scale. Currently, various DL approaches have been proposed to solve the ME issues and improve MER performance. In this survey, we provide a comprehensive review of deep MER and define a new taxonomy for the field encompassing all aspects of MER based on DL, including datasets, each step of the deep MER pipeline, and performance comparisons of the most influential methods. The basic approaches and advanced developments are summarized and discussed for each aspect. Additionally, we conclude the remaining challenges and potential directions for the design of robust MER systems. Finally, ethical considerations in MER are discussed. To the best of our knowledge, this is the first survey of deep MER methods, and this survey can serve as a reference point for future MER research.

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“…SMIC-E is an extension of SMIC (Pfister et al 2011), which is also the first publicly available spontaneous micro-expression database for recognition tasks. Since then, lots of databases are one by one released including MEVIEW (Husák, Cech, and Matas 2017), CAS(ME) 2 (Qu et al 2017), SAMM-Long (Yap, Kendrick, and Yap 2020), SMIC-E-Long (Tran et al 2021), and CAS(ME) 3 (Li et al 2022). We summarize the statistical information of these facial expression databases for DFES in Table 1.…”

Section: Introductionmentioning

confidence: 99%

SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos

Xu¹,

Zong²,

Zheng³

et al. 2022

Preprint

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In this paper, we present a large-scale, multi-source, and unconstrained database called SDFE-LV for spotting the onset and offset frames of a complete dynamic facial expression from long videos, which is known as the topic of dynamic facial expression spotting (DFES) and a vital prior step for lots of facial expression analysis tasks. Specifically, SDFE-LV consists of 1,191 long videos, each of which contains one or more complete dynamic facial expressions. Moreover, each complete dynamic facial expression in its corresponding long video was independently labeled for five times by 10 welltrained annotators. To the best of our knowledge, SDFE-LV is the first unconstrained large-scale database for the DFES task whose long videos are collected from multiple realworld/closely real-world media sources, e.g., TV interviews, documentaries, movies, and we-media short videos. Therefore, DFES tasks on SDFE-LV database will encounter numerous difficulties in practice such as head posture changes, occlusions, and illumination. We also provided a comprehensive benchmark evaluation from different angles by using lots of recent state-of-the-art deep spotting methods and hence researchers interested in DFES can quickly and easily get started. Finally, with the deep discussions on the experimental evaluation results, we attempt to point out several meaningful directions to deal with DFES tasks and hope that DFES can be better advanced in the future. In addition, SDFE-LV will be freely released for academic use only as soon as possible once the paper is accepted.

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CAS(ME)<sup>3</sup>: A Third Generation Facial Spontaneous Micro-Expression Database with Depth Information and High Ecological Validity

Cited by 47 publications

References 80 publications

Oxytocin Impairs the Recognition of Micro-Expressions of Surprise and Disgust

Oxytocin Impairs the Recognition of Micro-Expressions of Surprise and Disgust

Deep Learning for Micro-Expression Recognition: A Survey

SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos

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