To balance: balanced micro-expression recognition

Zhang, Ren; He, Ning; Wu, Ying; He, Yuzhe; Kang, Yan

doi:10.1007/s00530-021-00842-1

Cited by 3 publications

(1 citation statement)

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“…The process of micro-expression recognition mainly includes the two steps of feature extraction and classification, and the goodness of extracted features [7] is crucial to the result of micro-expression recognition. Depending on the different ways of extracting features, feature extraction can be divided into manual and automatic methods [8]. The manual method extracts micro-expression features from video or image samples by hand and then classifies them using a classifier.…”

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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Section: Introductionmentioning

confidence: 99%