A survey: facial micro-expression recognition

Takalkar, Madhumita A.; Wu, Qiang; Chaczko, Zenon

doi:10.1007/s11042-017-5317-2

Cited by 94 publications

(41 citation statements)

References 47 publications

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“…They present currently available 3D/4D face databases and the existing 3D/4D data-oriented FER systems in detail. [27] targets facial micro-expression recognition. Geometry-based and a appearance-based automatic FER systems are utilised in [28].…”

Section: Differences With Existing Survey and Contributionsmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.

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Section: Differences With Existing Survey and Contributionsmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

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“…music genre, user's playing history) but is also possible to track users' visual and auditory feedbacks in real-time. Facial expression detection, tracking, and classification of facial expressions have been widely studied [40]- [43] and even micro-expression detection [44], [45] for digging hidden emotions is becoming possible. Many efforts have been made from the music synthesis community [46]- [48] and a novel autoencoder framework was recently proposed [49].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Music Sequence Prediction with Mixture Hidden Markov Models

Choi

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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Recommendation systems that automatically generate personalized music playlists for users have attracted tremendous attention in recent years. Nowadays, most music recommendation systems rely on item-based or user-based collaborative filtering or content-based approaches. In this paper, we propose a novel mixture hidden Markov model (HMM) for music play sequence prediction. We compare the mixture model with state-of-the-art methods and evaluate the predictions quantitatively and qualitatively on a large-scale real-world dataset in a Kaggle competition. Results show that our model significantly outperforms traditional methods as well as other competitors. We conclude by envisioning a next-generation music recommendation system that integrates our model with recent advances in deep learning, computer vision, and speech techniques, and has promising potential in both academia and industry.

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“…"Most lies succeed because no one goes through the work to figure out how to catch them" -Paul Ekman (1934-) Different from facial expressions, Micro-expressions (MEs) are subtle and spontaneous facial muscle movements that usually last less than 200ms, which cannot be hidden even for professional actors [7]. Therefore, MEs are considered as the real reflections of human emotion [30] and are especially useful in high-risk situations, e.g., depression recovery test [54], negotiation with terrorists [39], criminal investigations [7]. Take the depression recovery test as an example.…”

Section: Introductionmentioning

confidence: 99%

AU-assisted Graph Attention Convolutional Network for Micro-Expression Recognition

Xie

Shuai

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

114

111

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Micro-expressions (MEs) are important clues for reflecting the real feelings of humans, and micro-expression recognition (MER) can thus be applied in various real-world applications. However, it is difficult to perceive and interpret MEs correctly. With the advance of deep learning technologies, the accuracy of micro-expression recognition is improved but still limited by the lack of large-scale datasets. In this paper, we propose a novel micro-expression recognition approach by combining Action Units (AUs) and emotion category labels. Specifically, based on facial muscle movements, we model different AUs based on relational information and integrate the AUs recognition task with MER. Besides, to overcome the shortcomings of limited and imbalanced training samples, we propose a data augmentation method that can generate nearly indistinguishable image sequences with AU intensity of real-world micro-expression images, which effectively improve the performance and are compatible with other micro-expression recognition methods. Experimental results on three mainstream micro-expression datasets, i.e., CASME II, SAMM, and SMIC, manifest that our approach outperforms other state-of-the-art methods on both single database and cross-database micro-expression recognition. CCS CONCEPTS • Computing methodologies → Neural networks; Computer vision; Image representations.

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A survey: facial micro-expression recognition

Cited by 94 publications

References 47 publications

Facial Expression Recognition: A Survey

Facial Expression Recognition: A Survey

Music Sequence Prediction with Mixture Hidden Markov Models

AU-assisted Graph Attention Convolutional Network for Micro-Expression Recognition

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