Name
Title | ||
|---|---|---|
Double Complete D-Lbp With Extreme Learning Machine Auto-Encoder And Cascade Forest For Facial Expression Analysis |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Facial expression recognition, local binary patterns, feature extraction, extreme learning machine auto-encoder, cascade forest |
Field | DocType | ISSN |
Facial recognition system,Autoencoder,Pattern recognition,Feature selection,Computer science,Extreme learning machine,Local binary patterns,Feature extraction,Facial expression,Artificial intelligence,Discriminative model | Conference | 1522-4880 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
3 | ||