Exemplar-embed complex matrix factorization for facial expression recognition
Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages
1837-1841
Date Issued
2017
Author(s)
Abstract
This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wirtinger's calculus was employed to determine derivatives. The gradient descent method was utilized to solve the complex optimization problem. Experiments on facial expression recognition verified the effectiveness of the proposed EE-CMF. It provides consistently better recognition results than standard NMFs.
Type
conference paper