{"title":"面部表情:面部区域的可分辨性及其与生物特征识别的关系","authors":"Elisa Barroso, G. Santos, Hugo Proença","doi":"10.1109/CIBIM.2013.6607918","DOIUrl":null,"url":null,"abstract":"Facial expressions result from movements of muscular action units, in response to internal emotion states or perceptions, and it has been shown that they decrease the performance of face-based biometric recognition techniques. This paper focuses in the recognition of facial expressions and has the following purposes: 1) confirm the suitability of using dense image descriptors widely known in biometrics research (e.g., local binary patterns and histogram of oriented gradients) to recognize facial expressions; 2) compare the effectiveness attained when using different regions of the face to recognize expressions; 3) compare the effectiveness attained when the identity of subjects is known/unknown, before attempting to recognize their facial expressions.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Facial expressions: Discriminability of facial regions and relationship to biometrics recognition\",\"authors\":\"Elisa Barroso, G. Santos, Hugo Proença\",\"doi\":\"10.1109/CIBIM.2013.6607918\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Facial expressions result from movements of muscular action units, in response to internal emotion states or perceptions, and it has been shown that they decrease the performance of face-based biometric recognition techniques. This paper focuses in the recognition of facial expressions and has the following purposes: 1) confirm the suitability of using dense image descriptors widely known in biometrics research (e.g., local binary patterns and histogram of oriented gradients) to recognize facial expressions; 2) compare the effectiveness attained when using different regions of the face to recognize expressions; 3) compare the effectiveness attained when the identity of subjects is known/unknown, before attempting to recognize their facial expressions.\",\"PeriodicalId\":286155,\"journal\":{\"name\":\"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-04-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIBIM.2013.6607918\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIBIM.2013.6607918","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Facial expressions: Discriminability of facial regions and relationship to biometrics recognition
Facial expressions result from movements of muscular action units, in response to internal emotion states or perceptions, and it has been shown that they decrease the performance of face-based biometric recognition techniques. This paper focuses in the recognition of facial expressions and has the following purposes: 1) confirm the suitability of using dense image descriptors widely known in biometrics research (e.g., local binary patterns and histogram of oriented gradients) to recognize facial expressions; 2) compare the effectiveness attained when using different regions of the face to recognize expressions; 3) compare the effectiveness attained when the identity of subjects is known/unknown, before attempting to recognize their facial expressions.