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• W2895889813 abstract "Smile is one of the most frequently appeared facial expressions of human beings. It is an explicit social signal to speculate on humans' emotion. Interpretation of infants smile behavior has received much attention in the field of psychology and pediatric medicine, i.e. Autism Spectral Disorder (ASD). Due to low efficiency of manual emotion encoding of infant facial images, automatic smile detection system has drawn many researchers' attention. However, lack of large amount of labeled data hinders the research progress in this field. In this paper, a novel dataset named RCLA&NBH_Smile Dataset was presented. Thirty four infants facial expression videos were recorded during mother-infant interaction and over 77,000 frames were manually labeled. For developing an automatic smile analysis system, we proposed a Convolutional Neural Network (CNN) based method for smile detection. We firstly evaluated our proposed method on two public datasets, GENKI-4K and CelebA, and it achieved satisfactory result of mean accuracy of 94.55% and 92.60%, respectively. Experiments on RCLA&NBH_Smile dataset show that our proposed approach can achieve the mean accuracy of 87.16% and F1-score of 62.54%, respectively." @default.
• W2895889813 created "2018-10-26" @default.
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• W2895889813 date "2018-10-19" @default.
• W2895889813 modified "2023-10-13" @default.
• W2895889813 title "Automatic Smile Detection of Infants in Mother-Infant Interaction via CNN-based Feature Learning" @default.
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• W2895889813 doi "https://doi.org/10.1145/3267935.3267951" @default.
• W2895889813 hasPublicationYear "2018" @default.
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