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篇名 |
Image Sequence Facial Expression Recognition Based on Deep Residual Network
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並列篇名 | Image Sequence Facial Expression Recognition Based on Deep Residual Network |
作者 | Junsuo Qu、Ruijun Zhang、Zhiwei Zhang、Ning Qiao、Jeng-Shyang Pan |
英文摘要 | A sequence of facial expression images can provide rich texture information and motion information about facial expression changes. Combining traditional manual designed feature extraction methods with learning-based methods, this paper proposes an image sequence facial expression recognition algorithm based on deep residual network. Feature extraction is performed for each frame image, where the local binary pattern (LBP) map of the facial expression image is used as the input of the network, and the deep residual network model is used as the feature extractor for the image sequence. Then, each frame image feature is connected to a feature vector as the feature representation of the image sequence. Further, the image sequence is used as the input of the long shortterm memory (LSTM) network, and the classification result is obtained through model training. Experimental results demonstrate the effectiveness of the proposed algorithm, where high recognition rates are observed based on both FER-2013 and AFEW6 datasets. |
起訖頁 | 1579-1587 |
關鍵詞 | Facial expression recognition、Local binary mode、Depth residual network、Long short-term memory |
刊名 | 網際網路技術學刊 |
期數 | 202011 (21:6期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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