title: Pain expression recognition based on pLSA model.
abstract: We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: "no pain," "slight pain," "moderate pain," and " severe pain." First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness.
authors: Department of Information Management, Hunan University of Finance and Economics, Changsha 410205, China.
authors: Shaoping
authors: S
authors: Zhu
mesh_terms: D000465:Algorithms; D005149:Facial Expression; D005260:Female; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D010146:Pain / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012720:Severity of Illness Index