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• W3040018512 abstract "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 short-term 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." @default.
• W3040018512 created "2020-07-10" @default.
• W3040018512 creator A5065677543 @default.
• W3040018512 creator A5072074332 @default.
• W3040018512 date "2020-01-01" @default.
• W3040018512 modified "2023-10-16" @default.
• W3040018512 title "Facial Micro-Expression Recognition Using Two-Dimensional Landmark Feature Maps" @default.
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• W3040018512 doi "https://doi.org/10.1109/access.2020.3006958" @default.
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