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• W4295088477 abstract "Facial expressions are divided into micro- and macro-expressions. Micro-expressions are low-intensity emotions presented for a short moment of about 0.25 s, whereas macro-expressions last up to 4 s. To derive micro-expressions, participants are asked to suppress their emotions as much as possible while watching emotion-inducing videos. However, it is a challenging process, and the number of samples collected tends to be less than those of macro-expressions. Because training models with insufficient data may lead to decreased performance, this study proposes two ways to solve the problem of insufficient data for micro-expression training. The first method involves N-step pre-training, which performs multiple transfer learning from action recognition datasets to those in the facial domain. Second, we propose Décalcomanie data augmentation, which is based on facial symmetry, to create a composite image by cutting and pasting both faces around their center lines. The results show that the proposed methods can successfully overcome the data shortage problem and achieve high performance." @default.
• W4295088477 created "2022-09-10" @default.
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• W4295088477 date "2022-09-03" @default.
• W4295088477 modified "2023-09-25" @default.
• W4295088477 title "N-Step Pre-Training and Décalcomanie Data Augmentation for Micro-Expression Recognition" @default.
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• W4295088477 doi "https://doi.org/10.3390/s22176671" @default.
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