A micro expression recognition method integrating LBP and parallel attention mechanism

This research proposes a micro expression recognition network that incorporates LBP and parallel attention method to address the issues of small feature discrimination, background noise interference, and weak intensity of facial micro-expression changes. The network inputs the RGB image into the densely connected improved Shuffle Stage branch to extract the global features of the face and enhance the association of contextual semantic information. The LBP image is input into the local texture feature branch composed of a multi-scale layered convolutional neural network to extract detailed information. Following extraction of the dual-branch feature, the network backend implements a parallel attention technique to enhance feature fusion capabilities, reduce background noise, and concentrate on the micro-expression feature's interest region. The proposed method is tested on three public data sets including CASME, CASME II and SMIC, and the recognition is accurate The rates reached 85.18%, 74.53% and 81.19% respectively. The experimental results show that the proposed method effectively improves the accuracy of micro expression recognition, which is better than many current advanced methods.