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首页> 外文期刊>IEICE transactions on information and systems >Unconstrained Facial Expression Recognition Based on Feature Enhanced CNN and Cross-Layer LSTM
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Unconstrained Facial Expression Recognition Based on Feature Enhanced CNN and Cross-Layer LSTM

机译：基于特征增强的CNN和跨层LSTM的无约束面部表情识别

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摘要

LSTM network have shown to outperform in facial expression recognition of video sequence. In view of limited representation ability of single-layer LSTM, a hierarchical attention model with enhanced feature branch is proposed. This new network architecture consists of traditional VGG-16-FACE with enhanced feature branch followed by a cross-layer LSTM. The VGG-16-FACE with enhanced branch extracts the spatial features as well as the cross-layer LSTM extracts the temporal relations between different frames in the video. The proposed method is evaluated on the public emotion databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.
机译：LSTM网络已显示在视频序列的面部表情识别中表达概率。鉴于单层LSTM的表示能力有限，提出了具有增强特征分支的分层注意模型。这种新的网络架构包括传统的VGG-16面，具有增强的特征分支，后跟一个横梁LSTM。具有增强分支的VGG-16面提取空间特征以及跨层LSTM提取视频中不同帧之间的时间关系。所提出的方法在主题和跨数据库任务中的公共情绪数据库上进行评估，并且优于最先进的方法。

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