MEGC 2019 – The Second Facial Micro-Expressions Grand Challenge

See, John; Yap, Moi Hoon; Li, Jingting; Hong, Xiaopeng; Wang, Sujing

doi:10.1109/fg.2019.8756611

Cited by 114 publications

(66 citation statements)

References 23 publications

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“…On the other hand, for a few years, a micro-expression grand challenge (MEGC) has been held to promote the competitive development of FME recognition techniques [37]. In MEGC, many FER techniques are evaluated using micro-expression-specific datasets such as SMIC [24], CASME II [25], SAMM [36], and their composite dataset.…”

Section: Related Workmentioning

confidence: 99%

Facial Micro-Expression Recognition Using Two-Dimensional Landmark Feature Maps

Choi

Song

2020

IEEE Access

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Emotion recognition based on facial expressions is very important for effective interaction of humans with artificial intelligence (AI) systems such as social robots. On the other hand, in real environment, it is much harder to recognize facial micro-expressions (FMEs) than facial general-expressions having rich emotions. In this paper, we propose a two-dimensional (2D) landmark feature map for effectively recognizing such FMEs. The proposed 2D landmark feature map (LFM) is obtained by transforming conventional coordinate-based landmark information into 2D image information. LFM is designed to have an advantageous property independent of the intensity of facial expression change. Also, we propose an LFM-based emotion recognition method that is an integrated framework of convolutional neural network (CNN) and long shortterm memory (LSTM). Experimental results show that the proposed method achieves about 71% and 74% in the well-known micro-expression datasets, i.e., SMIC and CASME II, respectively, which outperforms the conventional methods. The performance of the proposed method was also verified through experiments on composite micro-expression dataset, which consists of SMIC, CAMSE II and SAMM, and cross-dataset validation using SMIC and CAMSE II. In addition, we prove that the proposed method is independent of facial expression intensity through an experiment on CK+ dataset. Finally, we demonstrate that the proposed method is valid even for the MAHNOB-HCI and MEVIEW datasets that are produced to monitor actual and wild emotional responses.

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Section: Related Workmentioning

confidence: 99%

Facial Micro-Expression Recognition Using Two-Dimensional Landmark Feature Maps

Choi

Song

2020

IEEE Access

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“…As the micro-expression community always uses CASME2 [37], SMIC [16] and SAMM [2] databases as evaluation standards for recognition tasks [24,31], we adopt this custom in our paper. The CASME2 dataset has 249 micro-expressions from 26 subjects, with the average age of 22.03 years old at 200 fps.…”

Section: Experiments 41 Experiments Conditionmentioning

confidence: 99%

“…First, we test our proposed framework on the CASME2, SMIC and SAMM databases separately. Second, we test our framework on Composite Database Evaluation (CDE) task [24,31], i.e., samples from all databases are combined into a single composite database based on the reduced emotion classes. Specifically, the samples of happiness are given positive labels and the labels of surprise samples are unchanged.…”

Section: Experiments 41 Experiments Conditionmentioning

confidence: 99%

Learning from Macro-expression

Xia

Wang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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As one of the most important forms of psychological behaviors, micro-expression can reveal the real emotion. However, the existing labeled micro-expression samples are limited to train a high performance micro-expression classifier. Since micro-expression and macro-expression share some similarities in facial muscle movements and texture changes, in this paper we propose a microexpression recognition framework that leverages macro-expression samples as guidance. Specifically, we first introduce two Expression-Identity Disentangle Network, named MicroNet and MacroNet, as the feature extractor to disentangle expression-related features for micro and macro expression samples. Then MacroNet is fixed and used to guide the fine-tuning of MicroNet from both label and feature space. Adversarial learning strategy and triplet loss are added upon feature level between the MicroNet and MacroNet, so the MicroNet can efficiently capture the shared features of microexpression and macro-expression samples. Loss inequality regularization is imposed to the label space to make the output of Mi-croNet converge to that of MicroNet. Comprehensive experiments on three public spontaneous micro-expression databases, i.e., SMIC, CASME2 and SAMM demonstrate the superiority of the proposed method. CCS CONCEPTS • Human-centered computing → HCI design and evaluation methods.

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“…Besides, a combination of different datasets increased the number of subjects and samples, which is beneficial to the data-driven methods and deep-learning methods. The fundamental works of 1st Micro-Expression Grand Challenge (MEGC2018) [53] , 2nd Micro-Expression Grand Challenge (MEGC2019) [54] , and [55] facilitate the development of CDMER. Macro to Micro Transfer Learning [56] utilizes transfer learning to implement CNN from big macro-expression datasets to small ME datasets, ranking top in MEGC2018.…”

Section: Related Workmentioning

confidence: 99%

Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition

et al. 2019

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Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his or her genuine emotions. Recently, ME recognition has attracted increasing attention due to its potential applications such as clinical diagnosis, business negotiation, interrogations, and security. However, it is expensive to build large scale ME datasets, mainly due to the difficulty of inducing spontaneous MEs. This limits the application of deep learning techniques which require lots of training data.In this paper, we propose a simple, efficient yet robust descriptor called Extended Local Binary Patterns on Three Orthogonal Planes (ELBPTOP) for ME recognition. ELBPTOP consists of three complementary binary descriptors: LBPTOP and two novel ones Radial Difference LBPTOP (RDLBPTOP) and Angular Difference LBPTOP (ADLBPTOP), which explore the local second order information along the radial and angular directions contained in ME video sequences. ELBPTOP is a novel ME descriptor inspired by unique and subtle facial movements. It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition. In addition, by firstly introducing Whitened Principal Component Analysis (WPCA) to ME recognition, we can further obtain more compact and discriminative feature representations, then achieve significantly computational savings.Extensive experimental evaluation on three popular spontaneous ME datasets SMIC, CASME II and SAMM show that our proposed ELBPTOP approach significantly outperforms the previous state-of-the-art on all three single evaluated datasets and achieves promising results on cross-database recognition. Our code will be made available.Index Terms-Micro-expression recognition, local binary pattern, feature extraction. ADLBPTOP 2,4 + LBPTOP 3,4 + RDLBPXOT 3,4,1 Acc(%):73.94 ADLBPTOP 1,8 + LBPTOP 3,4 + RDLBPXOT 4,4,3 Acc(%):69.06 SMIC CASME II LBPTOP 3,4 Acc(%):71.55 ADLBPTOP 2,4 Acc(%):70.20 RDLBPXOT 3,4,1 Acc(%):69.24 LBPTOP 3,4 Acc(%):65.16 ADLBPTOP 1,8 Acc(%):63.73 RDLBPXOT 4,4,3 Acc(%):62.57

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MEGC 2019 – The Second Facial Micro-Expressions Grand Challenge

Cited by 114 publications

References 23 publications

Facial Micro-Expression Recognition Using Two-Dimensional Landmark Feature Maps

Facial Micro-Expression Recognition Using Two-Dimensional Landmark Feature Maps

Learning from Macro-expression

Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition

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