{"title":"基于民族人脸图像数据库的快速搜索算法","authors":"BaoWei Hou, Rui Zheng, Guosheng Yang","doi":"10.1109/ICSESS.2014.6933633","DOIUrl":null,"url":null,"abstract":"The current popular image features index structure can be divided into tree-based structures, hash-based structures and machine learning based structures. In face recognition, selecting the appropriate image feature indexing structure to achieve large-scale face image matching has aways been a problem. In this paper, we present a global image features indexing method based on complete binary tree, using the ethnic facial image database, by contrast with the local sensitive hash(LSH), and principal component analysis (PCA) is adopted to extract facial image features for convienent. Experimental results show that the proposed method is superior to the local sensitive hashing in velocity.","PeriodicalId":6473,"journal":{"name":"2014 IEEE 5th International Conference on Software Engineering and Service Science","volume":"50 1","pages":"573-576"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Quick search algorithms based on ethnic facial image database\",\"authors\":\"BaoWei Hou, Rui Zheng, Guosheng Yang\",\"doi\":\"10.1109/ICSESS.2014.6933633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The current popular image features index structure can be divided into tree-based structures, hash-based structures and machine learning based structures. In face recognition, selecting the appropriate image feature indexing structure to achieve large-scale face image matching has aways been a problem. In this paper, we present a global image features indexing method based on complete binary tree, using the ethnic facial image database, by contrast with the local sensitive hash(LSH), and principal component analysis (PCA) is adopted to extract facial image features for convienent. Experimental results show that the proposed method is superior to the local sensitive hashing in velocity.\",\"PeriodicalId\":6473,\"journal\":{\"name\":\"2014 IEEE 5th International Conference on Software Engineering and Service Science\",\"volume\":\"50 1\",\"pages\":\"573-576\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE 5th International Conference on Software Engineering and Service Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSESS.2014.6933633\",\"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 IEEE 5th International Conference on Software Engineering and Service Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSESS.2014.6933633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quick search algorithms based on ethnic facial image database
The current popular image features index structure can be divided into tree-based structures, hash-based structures and machine learning based structures. In face recognition, selecting the appropriate image feature indexing structure to achieve large-scale face image matching has aways been a problem. In this paper, we present a global image features indexing method based on complete binary tree, using the ethnic facial image database, by contrast with the local sensitive hash(LSH), and principal component analysis (PCA) is adopted to extract facial image features for convienent. Experimental results show that the proposed method is superior to the local sensitive hashing in velocity.