Double Complete D-LBP with Extreme Learning Machine Auto-Encoder and Cascade Forest for Facial Expression Analysis
Fang Shen, Jing Liu, Peng Wu · 2018
Although the obtained accuracy on some lab-controlled facial expression datasets has been very high, the recognition of facial expressions in wild environments is still a challenging problem. Local Binary Patterns (LBP) is a widely used operator in facial expression recognition. However, there are few variations of LBP operators specifically designed for facial expression recognition. In this paper, we propose a novel representation approach called the Double Complete d-LBP (Double Cd-LBP) according to the characteristics of facial expressions. Two d-LBP are employed to represent details and the contour of faces separately, and complete LBP is used to take sign and magnitude components into account. Moreover, multi-scale LBP is exploited to obtain local texture and global information. We then use the extreme learning machine auto-encoder (ELM-AE) as the feature selection approach to learn the discriminative feature. Cascade forest is employed as the final decision classifier. Experiments conducted on the six facial expression databases, including both lab-controlled and wild environments databases, show that our method outperforms or on par with state-of-the-arts.