{"title":"形状检索比赛2008:3D面部扫描","authors":"F. T. Haar, M. Daoudi, R. Veltkamp","doi":"10.1109/SMI.2008.4547979","DOIUrl":null,"url":null,"abstract":"Three-Dimensional face recognition is a challenging task with a large number of proposed solutions [1, 2]. With variations in pose and expression the identification of a face scan based on 3D geometry is difficult. To improve on this task and to evaluate existing face matching methods large sets of 3D faces were constructed, such as the FRGC [3], BU-3DFE [4], and the GavabDB [5] database. When used in the same experimental way, these publicly available sets allow for a fair comparison of different methods. Usually, researchers compare the recognition rates (or identification rates) of different methods. To identify a person, its 3D face scan is enrolled as query in the database and if the most similar scan (other than the query) in the database belongs to the same person, he or she is identified correctly. For a set of queries, the recognition rate is computed as the average of zeros (no identification) and ones (correct identification). However, the recognition rate is a limited evaluation measure, because it considers merely the closest match of each query. In case you are using a database that contains two scans per expression per subject and you use each scan as query once, you are bound to find the similar scan on top of the ranked list. Such an experiment boosts the recognition rate, but gives no insight of the expression invariance of different methods. For that, an evaluation measure is required that takes a larger part of the ranked list into account. In this contest we compare different face matching methods using a large number of performance measures. As a test set we have used a processed subset of the GavabDB [5], which contains several expressions and pose variations per subject. 2 DATABASE For the retrieval contest of 3D faces we have used a subset of the GavabDB [5]. The GavabDB consists of Minolta Vi-700 laser range scans from 61 different subjects. The subjects, of which 45 are male and 16 are female, are all Caucasian. Each subject was scanned nine times for different poses and expressions, namely six neutral expression scans and three scans with an expression. The neutral scans include two different frontal scans, one scan while looking up ( +35 ), one scan while looking down ( -35 ), one scan from the right side ( +90 ), and one from the left side ( -90 ). The expression scans include one with a smile, one with a pronounced laugh, and an “arbitrary expression” freely chosen by the subject.","PeriodicalId":118774,"journal":{"name":"2008 IEEE International Conference on Shape Modeling and Applications","volume":"222 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"28","resultStr":"{\"title\":\"SHape REtrieval contest 2008: 3D face scans\",\"authors\":\"F. T. Haar, M. Daoudi, R. Veltkamp\",\"doi\":\"10.1109/SMI.2008.4547979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Three-Dimensional face recognition is a challenging task with a large number of proposed solutions [1, 2]. With variations in pose and expression the identification of a face scan based on 3D geometry is difficult. To improve on this task and to evaluate existing face matching methods large sets of 3D faces were constructed, such as the FRGC [3], BU-3DFE [4], and the GavabDB [5] database. When used in the same experimental way, these publicly available sets allow for a fair comparison of different methods. Usually, researchers compare the recognition rates (or identification rates) of different methods. To identify a person, its 3D face scan is enrolled as query in the database and if the most similar scan (other than the query) in the database belongs to the same person, he or she is identified correctly. For a set of queries, the recognition rate is computed as the average of zeros (no identification) and ones (correct identification). However, the recognition rate is a limited evaluation measure, because it considers merely the closest match of each query. In case you are using a database that contains two scans per expression per subject and you use each scan as query once, you are bound to find the similar scan on top of the ranked list. Such an experiment boosts the recognition rate, but gives no insight of the expression invariance of different methods. For that, an evaluation measure is required that takes a larger part of the ranked list into account. In this contest we compare different face matching methods using a large number of performance measures. As a test set we have used a processed subset of the GavabDB [5], which contains several expressions and pose variations per subject. 2 DATABASE For the retrieval contest of 3D faces we have used a subset of the GavabDB [5]. The GavabDB consists of Minolta Vi-700 laser range scans from 61 different subjects. The subjects, of which 45 are male and 16 are female, are all Caucasian. Each subject was scanned nine times for different poses and expressions, namely six neutral expression scans and three scans with an expression. The neutral scans include two different frontal scans, one scan while looking up ( +35 ), one scan while looking down ( -35 ), one scan from the right side ( +90 ), and one from the left side ( -90 ). The expression scans include one with a smile, one with a pronounced laugh, and an “arbitrary expression” freely chosen by the subject.\",\"PeriodicalId\":118774,\"journal\":{\"name\":\"2008 IEEE International Conference on Shape Modeling and Applications\",\"volume\":\"222 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"28\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Conference on Shape Modeling and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMI.2008.4547979\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Shape Modeling and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMI.2008.4547979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Three-Dimensional face recognition is a challenging task with a large number of proposed solutions [1, 2]. With variations in pose and expression the identification of a face scan based on 3D geometry is difficult. To improve on this task and to evaluate existing face matching methods large sets of 3D faces were constructed, such as the FRGC [3], BU-3DFE [4], and the GavabDB [5] database. When used in the same experimental way, these publicly available sets allow for a fair comparison of different methods. Usually, researchers compare the recognition rates (or identification rates) of different methods. To identify a person, its 3D face scan is enrolled as query in the database and if the most similar scan (other than the query) in the database belongs to the same person, he or she is identified correctly. For a set of queries, the recognition rate is computed as the average of zeros (no identification) and ones (correct identification). However, the recognition rate is a limited evaluation measure, because it considers merely the closest match of each query. In case you are using a database that contains two scans per expression per subject and you use each scan as query once, you are bound to find the similar scan on top of the ranked list. Such an experiment boosts the recognition rate, but gives no insight of the expression invariance of different methods. For that, an evaluation measure is required that takes a larger part of the ranked list into account. In this contest we compare different face matching methods using a large number of performance measures. As a test set we have used a processed subset of the GavabDB [5], which contains several expressions and pose variations per subject. 2 DATABASE For the retrieval contest of 3D faces we have used a subset of the GavabDB [5]. The GavabDB consists of Minolta Vi-700 laser range scans from 61 different subjects. The subjects, of which 45 are male and 16 are female, are all Caucasian. Each subject was scanned nine times for different poses and expressions, namely six neutral expression scans and three scans with an expression. The neutral scans include two different frontal scans, one scan while looking up ( +35 ), one scan while looking down ( -35 ), one scan from the right side ( +90 ), and one from the left side ( -90 ). The expression scans include one with a smile, one with a pronounced laugh, and an “arbitrary expression” freely chosen by the subject.