关键词: 面部表情识别, 深度学习, 分层双线性池化, 注意力机制, ResNet-50

Abstract:

Since facial expression images have subtle inter-class different information and intra-class public information, extracting discriminative local features becomes a key issue. Therefore, an Attention Hierarchical Bilinear Pooling Residual Network（AHBPRN） is proposed. The model uses an effective channel attention mechanism to explicitly model the importance of each channel. It assigns different weights to the output feature map and locates the significant regions according to the weight value. A new hierarchical bilinear pooling layer is added, which integrates multiple cross-layer bilinear features to capture the inter-layer part feature relations, and spatial pooling is carried out in the feature map in an end-to-end deep learning way, so that the proposed network model is more suitable for fine facial expression classification. Experiments on the designed network are conducted on FER-2013 and CK+datasets, respectively, and the highest recognition rates are 73.84% and 98.79%, respectively, which achieves competitive classification accuracy and is suitable for subtle facial expression image recognition tasks.

Key words: facial expression recognition, deep learning, hierarchical bilinear pooling, attention mechanism, ResNet-50