Robust Transferable Subspace Learning for Cross-Corpus Facial Expression Recognition

Dongliang CHEN  Peng SONG  Wenjing ZHANG  Weijian ZHANG  Bingui XU  Xuan ZHOU  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.10   pp.2241-2245
Publication Date: 2020/10/01
Publicized: 2020/07/20
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDL8074
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
facial expression recognition,  subspace learning,  transfer learning,  graph Laplacian,  

Full Text: PDF(1.2MB)>>
Buy this Article



Summary: 
In this letter, we propose a novel robust transferable subspace learning (RTSL) method for cross-corpus facial expression recognition. In this method, on one hand, we present a novel distance metric algorithm, which jointly considers the local and global distance distribution measure, to reduce the cross-corpus mismatch. On the other hand, we design a label guidance strategy to improve the discriminate ability of subspace. Thus, the RTSL is much more robust to the cross-corpus recognition problem than traditional transfer learning methods. We conduct extensive experiments on several facial expression corpora to evaluate the recognition performance of RTSL. The results demonstrate the superiority of the proposed method over some state-of-the-art methods.