Name
Abstract | ||
|---|---|---|
Pain is a transient physical reaction that exhibits on human faces. Automatic pain intensity estimation is of great importance in clinical and health-care applications. Pain expression is identified by a set of deformations of facial features. Hence, features are essential for pain estimation. In this paper, we propose a novel method that encodes low-level descriptors and powerful high-level deep features by a weighting process, to form an efficient representation of facial images. To obtain a powerful and compact low-level representation, we explore the way of using second-order pooling over the local descriptors. Instead of direct concatenation, we develop an efficient fusion approach that unites the low-level local descriptors and the high-level deep features. To the best of our knowledge, this is the first approach that incorporates the low-level local statistics together with the high-level deep features in pain intensity estimation. Experiments are evaluated on the benchmark databases of pain. The results demonstrate that the proposed low-to-high-level representation outperforms other methods and achieves promising results. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICPR.2018.8545244 | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) |
Field | DocType | ISSN |
Histogram,Computer vision,Task analysis,Pattern recognition,Computer science,Pooling,Feature extraction,Local statistics,Concatenation,Artificial intelligence,A-weighting,Discrete cosine transforms | Conference | 1051-4651 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruijing Yang | 1 | 1 | 1.02 |
| Xiaopeng Hong | 2 | 379 | 42.27 |
| Jinye Peng | 3 | 284 | 40.93 |
| Xiaoyi Feng | 4 | 229 | 38.15 |
| Guoying Zhao | 5 | 3767 | 166.92 |