{"title":"基于稀疏表示的人脸识别改进算法","authors":"Cemil Turan, S. Kadyrov, Diana Burissova","doi":"10.1109/COCONET.2018.8476916","DOIUrl":null,"url":null,"abstract":"This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.","PeriodicalId":250788,"journal":{"name":"2018 International Conference on Computing and Network Communications (CoCoNet)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An Improved Face Recognition Algorithm Based on Sparse Representation\",\"authors\":\"Cemil Turan, S. Kadyrov, Diana Burissova\",\"doi\":\"10.1109/COCONET.2018.8476916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.\",\"PeriodicalId\":250788,\"journal\":{\"name\":\"2018 International Conference on Computing and Network Communications (CoCoNet)\",\"volume\":\"101 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 International Conference on Computing and Network Communications (CoCoNet)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COCONET.2018.8476916\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 International Conference on Computing and Network Communications (CoCoNet)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COCONET.2018.8476916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
本文研究了一种基于稀疏表示的分类算法。人脸识别的准确性和评估时间是两个关键的性能指标。本文将改进的基于稀疏表示的分类算法与原始的基于稀疏表示的分类算法的性能进行了比较。使用Yale Face Database B进行MATLAB仿真,结果表明,改进的基于稀疏表示的分类算法在时间上有较好的表现。此外,作者研究并比较了在每个主题只有少量训练样本的情况下这些算法。
An Improved Face Recognition Algorithm Based on Sparse Representation
This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.