Exemplar-embed complex matrix factorization for facial expression recognition
Viet-Hang Duong, Yuan-Shan Lee, Jian–Jiun Ding, Bach-Tung Pham, Manh-Quan Bui, Pham The Bao, Jia‐Ching Wang · 2017
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.