Name
Abstract | ||
|---|---|---|
In this work we present a system that enables automatic estimation of Pain from image sequences with frontal views of faces. The system uses facial characteristic points to characterize different Action Units (AU) of pain and is able to operate in cluttered and dynamic scenes. Geometric features are computed using 22 facial characteristic points. We use k-NN classifier for classifying AU. Only action units relevant to pain are classified. Validation studies are done on UNBC McMaster Shoulder Pain Archive Database [8]. We also classify action unit intensities for evaluating pain intensity on a 16 point scale. Our system is simpler in design compared to the already reported works in literature. Our system reports AU intensities on a standard scale and also reports pain intensity to assess pain. We have achieved more than 84% accuracy for AU intensity levels and 87.4% area under ROC curve for pain assessment as compared to 84% of state-of-the-art scheme. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.803 | ICPR |
Keywords | Field | DocType |
pain expression,k-nn classifier,pain estimation,face recognition,geometric features,roc curve,facial characteristic points,estimation theory,emotion recognition,facial action unit,pain expression, geometric features, k-nn classifier, action units, roc curve,image sequences,geometric feature,action units,pain intensity evaluation | Computer vision,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Robot,Robotics | Conference |
ISSN | Citations | PageRank |
1051-4651 | 4 | 0.42 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zuhair Zafar | 1 | 4 | 0.42 |
| Nadeem Khan | 2 | 6 | 2.51 |