{"title":"通过面部动作单元评估疼痛强度","authors":"Zuhair Zafar, N. Khan","doi":"10.1109/ICPR.2014.803","DOIUrl":null,"url":null,"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.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"388 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":"{\"title\":\"Pain Intensity Evaluation through Facial Action Units\",\"authors\":\"Zuhair Zafar, N. Khan\",\"doi\":\"10.1109/ICPR.2014.803\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":142159,\"journal\":{\"name\":\"2014 22nd International Conference on Pattern Recognition\",\"volume\":\"388 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 22nd International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.2014.803\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.2014.803","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pain Intensity Evaluation through Facial Action Units
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.