{"title":"银色机器人的多姿态人脸检测","authors":"Jongmin Yoon, Jongju Shin, Daijin Kim","doi":"10.1109/URAI.2013.6677446","DOIUrl":null,"url":null,"abstract":"This paper presents a method which can detect multi-pose faces for robot. To overcome a disadvantage of conventional single-view face detection. we compute hyperplane using Linear Discriminant Analysis (LDA). These hyperplane are used to separate two different classes finely. This method could reduce false positive rate on branching nodes significantly, as well as the number of unnecessary branches. Consequently, overall speed of the detector could be improved about 24 percent compared to the case without hyperplane partition.","PeriodicalId":431699,"journal":{"name":"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-pose face detection for silver robots\",\"authors\":\"Jongmin Yoon, Jongju Shin, Daijin Kim\",\"doi\":\"10.1109/URAI.2013.6677446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a method which can detect multi-pose faces for robot. To overcome a disadvantage of conventional single-view face detection. we compute hyperplane using Linear Discriminant Analysis (LDA). These hyperplane are used to separate two different classes finely. This method could reduce false positive rate on branching nodes significantly, as well as the number of unnecessary branches. Consequently, overall speed of the detector could be improved about 24 percent compared to the case without hyperplane partition.\",\"PeriodicalId\":431699,\"journal\":{\"name\":\"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/URAI.2013.6677446\",\"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 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/URAI.2013.6677446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a method which can detect multi-pose faces for robot. To overcome a disadvantage of conventional single-view face detection. we compute hyperplane using Linear Discriminant Analysis (LDA). These hyperplane are used to separate two different classes finely. This method could reduce false positive rate on branching nodes significantly, as well as the number of unnecessary branches. Consequently, overall speed of the detector could be improved about 24 percent compared to the case without hyperplane partition.