{"title":"基于相对不变结构特征的模糊掌纹识别","authors":"G. Wang, Weibo Wei, Zhenkuan Pan","doi":"10.1109/CSCI.2015.15","DOIUrl":null,"url":null,"abstract":"A blurred palmprint recognition method based on Relative Invariant Structure Feature (RISF) is proposed in this paper to improve the low recognition accuracy of blurred palmprint. Firstly, the OSV decomposition model is used to obtain stable feature from blurred images. Next, a non-overlapping sampling method based on Structure Ratio (SR) for RISF is used to further improve the effectiveness of feature. Finally, Structural Similarity Index Measurement (SSIM) is introduced to measure the similarity of palmprints and judge the palmprint category for classification. Numerical experiments show that the proposed method is effective and better than some other classical algorithms.","PeriodicalId":417235,"journal":{"name":"2015 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Blurred Palmprint Recognition Based on Relative Invariant Structure Feature\",\"authors\":\"G. Wang, Weibo Wei, Zhenkuan Pan\",\"doi\":\"10.1109/CSCI.2015.15\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A blurred palmprint recognition method based on Relative Invariant Structure Feature (RISF) is proposed in this paper to improve the low recognition accuracy of blurred palmprint. Firstly, the OSV decomposition model is used to obtain stable feature from blurred images. Next, a non-overlapping sampling method based on Structure Ratio (SR) for RISF is used to further improve the effectiveness of feature. Finally, Structural Similarity Index Measurement (SSIM) is introduced to measure the similarity of palmprints and judge the palmprint category for classification. Numerical experiments show that the proposed method is effective and better than some other classical algorithms.\",\"PeriodicalId\":417235,\"journal\":{\"name\":\"2015 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCI.2015.15\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Computational Science and Computational Intelligence (CSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCI.2015.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Blurred Palmprint Recognition Based on Relative Invariant Structure Feature
A blurred palmprint recognition method based on Relative Invariant Structure Feature (RISF) is proposed in this paper to improve the low recognition accuracy of blurred palmprint. Firstly, the OSV decomposition model is used to obtain stable feature from blurred images. Next, a non-overlapping sampling method based on Structure Ratio (SR) for RISF is used to further improve the effectiveness of feature. Finally, Structural Similarity Index Measurement (SSIM) is introduced to measure the similarity of palmprints and judge the palmprint category for classification. Numerical experiments show that the proposed method is effective and better than some other classical algorithms.