期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 22, 期 3, 页码 626-640出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2019.2931351
关键词
Spatiotemporal phenomena; Feature extraction; Task analysis; Videos; Training; Strain; Deep learning; Micro-Expression Recognition; Spatiotemporal Modeling; Temporal Connectivity; Recurrent Convolutional Networks; Data Augmentation; Balanced Loss
资金
- National Natural Science Foundation of China [61702419, 61702491]
- Natural Science Basic Research Plan in Shaanxi Province of China [2018JQ6090]
Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two ways to extend the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, two temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-of-the-art methods.
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