Name
Abstract | ||
|---|---|---|
Image-based face pose estimation tries to estimate the facial direction with 2D images. It provides important information for many face recognition applications. However, it is a difficult task due to complex conditions and appearances. Deep learning method used in this field has the disadvantage of ignoring the natural structures of human faces. To solve this problem, a framework is proposed in this paper to estimate face poses with regression, which is based on deep learning and multi-modal feature loss (\\(M^2FL\\)). Different from current loss functions using only a single type of features, the descriptive power was improved by combining multiple image features. To achieve it, hypergraph-based manifold regularization was applied. In this way, the loss of face pose estimation was reduced. Experimental results on commonly-used benchmark datasets demonstrate the performance of \\(M^2FL\\). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-981-15-7981-3_39 | ICPCSEE (1) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanqiu Wu | 1 | 0 | 0.34 |
| Chaoqun Hong | 2 | 76 | 6.36 |
| Liang Chen | 3 | 258 | 28.02 |
| Zhiqiang Zeng | 4 | 139 | 16.35 |