Learning transferable non-negative feature representation for facial expression recognition
Ji Min Liang, Peng Song, Wenjing Zhang, Shaokai Li · Digital Signal Processing · 2023
Conventional facial expression recognition (FER) methods often assume that the training and test procedures are carried out on a single database, without considering the cross-database problem due to the difference in ages, collecting devices, nationalities, etc. To tackle the above challenge, we propose a novel transferable non-negative feature representation (TNFR) method in this work. The core ideas of TNFR are two-fold. First, we utilize the basis matrix as a transformation matrix to reduce the distribution discrepancy between two databases. Second, we utilize the label information to improve the discrimination ability of the model. In addition, we replace the original label matrix with a block-label matrix to make the model more flexible. Experiments on four benchmark databases are provided to demonstrate the effectiveness of the proposed TNFR method in comparison with state-of-the-art methods.