MERANet

Gajjala, Viswanatha Reddy; Reddy, Sai Prasanna Teja; Mukherjee, Snehasis; Dubey, Shiv Ram

doi:10.1145/3490035.3490260

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…In MMNet [ 44 ], Li et al introduced a Continuous Attention module and a Position Calibration module based on the vision transformer [ 45 ], allowing for efficient capture of ME features by focusing on local, subtle muscle movements. Recent methods have also focused on incorporating spatio-temporal and channel information to represent MEs better [ 46 , 47 , 48 ]. Wang et al [ 48 ] proposed a dual-stream spatio-temporal attention network (DSTAN) that captures MEs’ appearance features and discriminative motion areas.…”

Section: Related Workmentioning

confidence: 99%

Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition

Zhou

Huang

et al. 2023

Entropy

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Micro-expression recognition (MER) is challenging due to the difficulty of capturing the instantaneous and subtle motion changes of micro-expressions (MEs). Early works based on hand-crafted features extracted from prior knowledge showed some promising results, but have recently been replaced by deep learning methods based on the attention mechanism. However, with limited ME sample sizes, features extracted by these methods lack discriminative ME representations, in yet-to-be improved MER performance. This paper proposes the Dual-branch Attention Network (Dual-ATME) for MER to address the problem of ineffective single-scale features representing MEs. Specifically, Dual-ATME consists of two components: Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS). HARS uses prior knowledge to manually extract features from regions of interest (ROIs). Meanwhile, AARS is based on attention mechanisms and extracts hidden information from data automatically. Finally, through similarity comparison and feature fusion, the dual-scale features could be used to learn ME representations effectively. Experiments on spontaneous ME datasets (including CASME II, SAMM, SMIC) and their composite dataset, MEGC2019-CD, showed that Dual-ATME achieves better, or more competitive, performance than the state-of-the-art MER methods.

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Section: Related Workmentioning

confidence: 99%

Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition

Zhou

Huang

et al. 2023

Entropy

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“…The residual network can solve the gradient vanishing problem in deep neural networks through a residual connection [13]. Gajjala et al proposed a microexpression recognition method based on a 3D residual attention network [14]. However, the above methods did not consider the issue of imbalanced sample numbers in micro-expression datasets.…”

Section: Introductionmentioning

confidence: 99%

Micro-expression recognition based on euler video magnification and 3D residual network under imbalanced sample

Zhu,

He,

Yang

et al. 2024

Eng. Res. Express

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A student's verbal behavior plays a crucial role in education, while nonverbal behavior, such as micro-expressions, significantly improves teaching quality. To address the problem of small facial expression movements, imbalanced data categories, and lack of temporal information in static expressions, a micro-expression recognition method is proposed based on Eulerian Video Magnification (EVM) and a 3D Residual Network (3D ResNet) under imbalanced samples. Firstly, face detection in the Dlib library is used to locate the face in the micro-expression video sample and crop it. Secondly, the EVM is used to magnify the motion features in micro-expressions. Then, the 3D ResNet is used to extract spatio-temporal information from micro-expression video samples, and the Cyclical Focal Loss (CFL) function is introduced in the network training process to solve the class imbalance problem in micro-expression datasets. Finally, the roles of the EVM and the CFL function in recognizing micro-expressions by the 3D ResNet are analyzed. The experimental results on the Spontaneous Micro-expression Database (SMIC) and Chinese Academy of Sciences Micro-expression Database II (CASME II) demonstrate the effectiveness and superiority of this method. The proposed method can assist in teaching evaluation and promote the development of smart classrooms, and further research is needed on the storage and computing of the proposed method on devices.

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AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images

Anirudh¹,

Dubey

2022

Communications in Computer and Information Science

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MERANet

Cited by 10 publications

References 29 publications

Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition

Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition

Micro-expression recognition based on euler video magnification and 3D residual network under imbalanced sample

AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images

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