Micro-expression recognition algorithm based on separate long-term recurrent convolutional network
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摘要: 基于面部動態表情序列，針對靜態表情缺少時間信息等問題，將空間特征與時間特征融合，利用神經網絡在圖像分類領域良好的特征，對需要進行細節分析的表情序列進行處理，提出基于分離式長期循環卷積網絡(Separate long-term recurrent convolutional networks, S-LRCN)的微表情識別方法。首先選取微表情數據集提取面部圖像序列，引入遷移學習的方法，通過預訓練的卷積神經網絡模型提取表情幀的空間特征，降低網絡訓練中過擬合的危險，并將視頻序列的提取特征輸入長短期記憶網絡(Long short-team memory, LSTM)處理時域特征。最后建立學習者表情序列小型數據庫，將該方法用于輔助教學評價。Abstract: With the rapid development of machine learning and deep neural network and the popularization of intelligent devices, face recognition technology has rapidly developed. At present, the accuracy of face recognition has exceeded that of the human eyes. Moreover, the software and hardware conditions of large-scale popularization are available, and the application fields are widely distributed. As an important part of face recognition technology, facial expression recognition has been a widely studied subject in the fields of artificial intelligence, security, automation, medical treatment, and driving in recent years. Expression recognition, an active research area in human–computer interaction, involves informatics and psychology and has good research prospect in teaching evaluation. Micro-expression, which has great research significance, is a kind of short-lived facial expression that humans unconsciously make when trying to hide some emotion. Different from the general static facial expression recognition, to realize micro-expression recognition, besides extracting the spatial feature information of facial expression deformation in the image, the temporal-motion information of the continuous image sequence also needs to be considered. In this study, given that static expression features lack temporal information, so that the subtle changes in expression cannot be fully reflected, facial dynamic expression sequences were used to fuse spatial features and temporal features, and neural networks were used to provide good features in the field of image classification. Expression sequences were processed, and a micro-expression recognition method based on separate long-term recurrent convolutional network (S-LRCN) was proposed. First, the micro-expression data set was selected to extract the facial image sequence, and the transfer learning method was introduced to extract the spatial features of the expression frame through the pre-trained convolution neural network model, to reduce the risk of overfitting in the network training, and the extracted features of the video sequence were inputted into long short-term memory (LSTM) to process the temporal-domain features. Finally, a small database of learners’ expression sequences was established, and the method was used to assist teaching evaluation.
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Key words:
- micro-expression recognition /
- spatial-temporal features /
- LRCN /
- LSTM /
- education evaluation
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表 1 劃分情況
Table 1. Dataset classification
Classify CASME-Ⅱ Samples Happiness Happiness (32) 32 Surprise Surprise (28) 28 Disgust Disgust (63) 63 Repression Repression (27) 27 Others Others (99) 105 Sadness (4) Fear (2) 
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表 3 不同算法識別準確率
Table 3. Recognition accuracy of different algorithms
Methods Accuracy/% F1-Score/% LBP-TOP 52.6 42.6 STCLQP 58.6 58.0 CNN+LSTM 61.0 58.5 HOOF+LSTM 59.8 56.0 S-LRCN 65.7 60.8
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表 4 不同序列長度實驗效果
Table 4. Experimental results of different sequence lengths
Sequence length Accuracy/% F1-Score/% 6 62.0 56.6 10 65.7 60.8 15 63.1 58.6 30 56.5 49.6
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