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- W2783752027 abstract "The psychological research found that human eyes could serve as a sensitive indicator of emotional response. Pupillary response has been used to analyze the affective video content in previous studies, but the performance is not good enough. In this paper, we propose a novel method for implicit affective video tagging using pupillary response. The issue of pupil size difference between subjects has not been effectively solved, which seriously affected the performance of the implicit affective video tagging. In our method, we first define the pupil diameter baseline of each subject to diminish individual difference on pupil size. Besides, the probabilistic support vector machine (SVM) and long short term memory (LSTM) network are used to extract valuable information and output the probability estimates based on the proposed global features and sequence features obtained from the pupil dilation ratio time-series data, respectively. The final decision is made by combining the probability estimates from these two models based on the sum rule. In empirical validation, we evaluate the proposed method on a standard dataset MAHNOB-HCI. The experimental results show that the proposed method achieves better classification accuracy compared with the existing method." @default.
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- W2783752027 date "2018-01-01" @default.
- W2783752027 modified "2023-10-16" @default.
- W2783752027 title "Implicit Affective Video Tagging Using Pupillary Response" @default.
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- W2783752027 doi "https://doi.org/10.1007/978-3-319-73600-6_15" @default.
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