关键词: 微表情识别, Apex帧, 光流, 卷积自编码器, 支持向量机（SVM）

Abstract:

Aiming at the problem of cross-database micro-expression recognition, a micro-expression recognition method based on Apex frame optical flow and convolutional autoencoder is proposed. The method includes three parts：preprocessing, feature extraction and micro-expression classification. The preprocessing section performs Apex frame positioning, face detection and alignment on the micro-expressions. The feature extraction section first calculates the TVL1 optical flow of the pre-processed Apex frame, then uses the obtained horizontal and vertical optical flow component images to train the convolutional autoencoder to obtain the optimal structure and parameters, finally combines the two components from the features of the middle layer of the encoder as the features of the micro-expressions. In section of micro-expression classification, a Support Vector Machine（SVM） classifier is used to classify the features extracted in the previous step. The experimental results have been greatly improved compared to the baseline method（LBP-TOP）. Among them, UF1 has increased by 0.134 4, and UAR has increased by 0.140 6. This method provides new ideas for micro-expression features extraction and recognition.

Key words: micro-expression recognition, Apex frame, optical flow, convolutional autoencoder, Support Vector Machine（SVM）