DocumentCode :
2430086
Title :
Facial expression recognition for neonatal pain assessment
Author :
Lu, Guanming ; Li, Xiaonan ; Li, Haibo
Author_Institution :
Nanjing Univ. of Posts & Telecommun., Nanjing
fYear :
2008
fDate :
7-11 June 2008
Firstpage :
456
Lastpage :
460
Abstract :
Facial expressions are considered a critical factor in neonatal pain assessment. This paper attempts to apply modern facial expression recognition techniques to the task of distinguishing pain expression from non-pain expression. Firstly, 2D Gabor filter is applied to extract the expression features from facial images. Then we apply Adaboost as a feature selection tool to remove the redundant Gabor features. Finally, the Gabor features selected by Adaboost are fed into the support vector machines (SVMs) for final classification. 510 facial images are investigated by using SVMs. The best recognition rates of pain versus non-pain (85.29%), pain versus calm (94.24%), pain versus cry (78.24%) were obtained from an SVM with a polynomial kernel of degree 3. The results of this study indicate that the application of SVM technique in pain assessment is a promising area of investigation.
Keywords :
Gabor filters; face recognition; feature extraction; support vector machines; 2D Gabor filter; Adaboost; facial expression recognition; facial images; feature extraction; feature selection tool; neonatal pain assessment; non-pain expression; support vector machines; Biomedical imaging; Face recognition; Feature extraction; Gabor filters; Hospitals; Pain; Pediatrics; Support vector machine classification; Support vector machines; Testing; AdaBoost; Expression Recognition; Gabor filer; Neonatal Pain; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Signal Processing, 2008 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-2310-1
Electronic_ISBN :
978-1-4244-2311-8
Type :
conf
DOI :
10.1109/ICNNSP.2008.4590392
Filename :
4590392
Link To Document :
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