Cross-Database Evaluation of Pain Recognition from Facial Video

So far, all studies investigating the facial expression of pain have validated methods on the same database, whereas the cross-database performance is less considered. This may be due to poor performance of well-trained models on other databases. In this paper, we propose two distinct methods to cla...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) s. 181 - 186
Hlavní autori: Othman, Ehsan, Werner, Philipp, Saxen, Frerk, Al-Hamadi, Ayoub, Walter, Steffen
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2019
Predmet:
BioVid
cross-database
Deep learning
Face recognition
Feature extraction
Pain
pain assessment
Pipelines
Task analysis
X-ITE
ISSN:1849-2266
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:So far, all studies investigating the facial expression of pain have validated methods on the same database, whereas the cross-database performance is less considered. This may be due to poor performance of well-trained models on other databases. In this paper, we propose two distinct methods to classify based on the temporal information. To explore the generalization capability of pain recognition models, we do cross-database validations on two benchmark pain databases: BioVid and X-ITE. We also experiment with combining both databases. Experimental results (1) show that our methods can be successfully used to classify pain (both methods perform similarly well), (2) demonstrate that the performance is robust by verifying them cross-database, and (3) present that the performance of pain assessment is improved with more data (combined-database).
ISSN:1849-2266
DOI:10.1109/ISPA.2019.8868562