کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6863566 | 1439515 | 2018 | 38 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Micro-expression recognition with small sample size by transferring long-term convolutional neural network
ترجمه فارسی عنوان
تشخیص میکرو بیان با اندازه نمونه کوچک با انتقال شبکه عصبی کانولوشن طولانی مدت
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کلمات کلیدی
بیان میکرو، یادگیری عمیق، انتقال یادگیری، شبکه عصبی متقاطع،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, video clips of high spatial-temporal resolution are much more desired than still images to provide sufficient details. On the other hand, owing to the difficulties to collect and encode micro-expression data, it is small sample size. In this paper, we use only 560 micro-expression video clips to evaluate the proposed network model: Transferring Long-term Convolutional Neural Network (TLCNN). TLCNN uses Deep CNN to extract features from each frame of micro-expression video clips, then feeds them to Long Short Term Memory (LSTM) which learn the temporal sequence information of micro-expression. Due to the small sample size of micro-expression data, TLCNN uses two steps of transfer learning: (1) transferring from expression data and (2) transferring from single frame of micro-expression video clips, which can be regarded as “big data”. Evaluation on 560 micro-expression video clips collected from three spontaneous databases is performed. The results show that the proposed TLCNN is better than some state-of-the-art algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 312, 27 October 2018, Pages 251-262
Journal: Neurocomputing - Volume 312, 27 October 2018, Pages 251-262
نویسندگان
Su-Jing Wang, Bing-Jun Li, Yong-Jin Liu, Wen-Jing Yan, Xinyu Ou, Xiaohua Huang, Feng Xu, Xiaolan Fu,