{"title":"基于分块DCV和支持向量机的人耳识别","authors":"Hailong Zhao, Junyan Yi","doi":"10.4018/IJITN.2016040104","DOIUrl":null,"url":null,"abstract":"In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.","PeriodicalId":42285,"journal":{"name":"International Journal of Interdisciplinary Telecommunications and Networking","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Combining Block DCV and Support Vector Machine for Ear Recognition\",\"authors\":\"Hailong Zhao, Junyan Yi\",\"doi\":\"10.4018/IJITN.2016040104\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.\",\"PeriodicalId\":42285,\"journal\":{\"name\":\"International Journal of Interdisciplinary Telecommunications and Networking\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2016-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Interdisciplinary Telecommunications and Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJITN.2016040104\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TELECOMMUNICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Interdisciplinary Telecommunications and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJITN.2016040104","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TELECOMMUNICATIONS","Score":null,"Total":0}
Combining Block DCV and Support Vector Machine for Ear Recognition
In recent years, automatic ear recognition has become a popular research. Effective feature extraction is one of the most important steps in Content-based ear image retrieval applications. In this paper, the authors proposed a new vectors construction method for ear retrieval based on Block Discriminative Common Vector. According to this method, the ear image is divided into 16 blocks firstly and the features are extracted by applying DCV to the sub-images. Furthermore, Support Vector Machine is used as classifier to make decision. The experimental results show that the proposed method performs better than classical PCA+LDA, so it is an effective human ear recognition method.
期刊介绍：
The International Journal of Interdisciplinary Telecommunications and Networking (IJITN) examines timely and important telecommunications and networking issues, problems, and solutions from a multidimensional, interdisciplinary perspective for researchers and practitioners. IJITN emphasizes the cross-disciplinary viewpoints of electrical engineering, computer science, information technology, operations research, business administration, economics, sociology, and law. The journal publishes theoretical and empirical research findings, case studies, and surveys, as well as the opinions of leaders and experts in the field. The journal''s coverage of telecommunications and networking is broad, ranging from cutting edge research to practical implementations. Published articles must be from an interdisciplinary, rather than a narrow, discipline-specific viewpoint. The context may be industry-wide, organizational, individual user, or societal. Topics Covered: -Emerging telecommunications and networking technologies -Global telecommunications industry business modeling and analysis -Network management and security -New telecommunications applications, products, and services -Social and societal aspects of telecommunications and networking -Standards and standardization issues for telecommunications and networking -Strategic telecommunications management -Telecommunications and networking cultural issues and education -Telecommunications and networking hardware and software design -Telecommunications investments and new ventures -Telecommunications network modeling and design -Telecommunications regulation and policy issues -Telecommunications systems economics
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