Facial Micro-Expressions Grand Challenge 2018: Evaluating Spatio-Temporal Features for Classification of Objective Classes

Merghani, Walied; Davison, Adrian K.; Yap, Moi Hoon

doi:10.1109/fg.2018.00104

Cited by 26 publications

(19 citation statements)

References 13 publications

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“…To test the robustness of our deep neural network architecture and the ability to learn significant features from samples, we use two cross‐domain protocols: Holdout‐database Evaluation (HDE) and Composite‐database Evaluation (CDE). The HDE and CDE are used in the Micro‐Expression Grand Challenge (MEGC) 2018 [34]. They are tasks A and B in MEGC 2018, respectively, and use CASME II and SAMM dataset together.…”

Section: Resultsmentioning

confidence: 99%

TSNN:Three‐StreamCombining2Dand3DConvolutional Neural Network for Micro‐Expression Recognition

Guo

2020

IEEJ Transactions Elec Engng

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Non-member Fan Guo a , Non-member Facial micro-expression recognition is a natural mechanism of facial behavior with subtle muscle movements and short duration, which is widely considered to be hard to recognize. In this paper, we propose the temporal sampling deformation (TSD) to normalize the temporal lengths and conserve time domain information for micro-expression sequences. A three-stream combining 2D and 3D convolutional neural network (TSNN) is also proposed to capture the features of micro-expressions and classify the expressions as well. The proposed network has two variants TSNN-IF and TSNN-LF, which can automatically learn spatial and temporal features at the same time. Single domain experiments and cross-domain experiments are also performed in the three benchmark datasets (chinese academy of sciences micro-expression II (CASME II), spontaneous micro-expression database (SMIC), and spontaneous micro-facial movement dataset (SAMM)) to verify the effectiveness and validity of the proposed framework. Comprehensive results and ablation studies show that the proposed method can achieve comparable or even better results compared with other state-of-the-art methods for micro-expression recognition.

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Section: Resultsmentioning

confidence: 99%

TSNN:Three‐StreamCombining2Dand3DConvolutional Neural Network for Micro‐Expression Recognition

Guo

2020

IEEJ Transactions Elec Engng

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“…Two protocols – Hold-out Database Evaluation (HDE) and Composite Database Evaluation (CDE), were proposed in the challenge, using the CASME II and SAMM databases. The reported performances (Khor et al, 2018; Merghani et al, 2018; Peng et al, 2018) were poorer than most other works that apply only to single databases, indicating that future methods need to be more robust across domains.…”

Section: Challengesmentioning

confidence: 88%

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

et al. 2018

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Over the last few years, automatic facial micro-expression analysis has garnered increasing attention from experts across different disciplines because of its potential applications in various fields such as clinical diagnosis, forensic investigation and security systems. Advances in computer algorithms and video acquisition technology have rendered machine analysis of facial micro-expressions possible today, in contrast to decades ago when it was primarily the domain of psychiatrists where analysis was largely manual. Indeed, although the study of facial micro-expressions is a well-established field in psychology, it is still relatively new from the computational perspective with many interesting problems. In this survey, we present a comprehensive review of state-of-the-art databases and methods for micro-expressions spotting and recognition. Individual stages involved in the automation of these tasks are also described and reviewed at length. In addition, we also deliberate on the challenges and future directions in this growing field of automatic facial micro-expression analysis.

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“…Consequently, a minimum threshold of duration for the expressive events has been established. Referring to the literature, intensity peaks too short to be considered facial expressions (<1 s) were excluded from the analysis [76,77]. Given this filtered signal, a set of signal processing features from the AUs activation peaks characterizing the interactions were then extracted.…”

Section: Signal Processingmentioning

confidence: 99%

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

Alvari

Furlanello

Venuti

2021

JCM

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Time is a key factor to consider in Autism Spectrum Disorder. Detecting the condition as early as possible is crucial in terms of treatment success. Despite advances in the literature, it is still difficult to identify early markers able to effectively forecast the manifestation of symptoms. Artificial intelligence (AI) provides effective alternatives for behavior screening. To this end, we investigated facial expressions in 18 autistic and 15 typical infants during their first ecological interactions, between 6 and 12 months of age. We employed Openface, an AI-based software designed to systematically analyze facial micro-movements in images in order to extract the subtle dynamics of Social Smiles in unconstrained Home Videos. Reduced frequency and activation intensity of Social Smiles was computed for children with autism. Machine Learning models enabled us to map facial behavior consistently, exposing early differences hardly detectable by non-expert naked eye. This outcome contributes to enhancing the potential of AI as a supportive tool for the clinical framework.

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Facial Micro-Expressions Grand Challenge 2018: Evaluating Spatio-Temporal Features for Classification of Objective Classes

Cited by 26 publications

References 13 publications

TSNN:Three‐StreamCombining2Dand3DConvolutional Neural Network for Micro‐Expression Recognition

TSNN:Three‐StreamCombining2Dand3DConvolutional Neural Network for Micro‐Expression Recognition

A Survey of Automatic Facial Micro-Expression Analysis: Databases, Methods, and Challenges

Is Smiling the Key? Machine Learning Analytics Detect Subtle Patterns in Micro-Expressions of Infants with ASD

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