Verónica Almeida, M. Dutta, C. Travieso, Anushikha Singh, J. B. Alonso
{"title":"基于面部图像的自动年龄检测","authors":"Verónica Almeida, M. Dutta, C. Travieso, Anushikha Singh, J. B. Alonso","doi":"10.1109/CCINTELS.2016.7878211","DOIUrl":null,"url":null,"abstract":"In this paper, the proposed implementation of a soft-biometric system for automatic age detection from facial images is described. In order to do this, the method followed was that of a classical biometric system. The first step is preprocessing, to enhance the feature extraction. The next step is the parameterization, where techniques like wavelet transformed, discrete cosine transformed or local binary patterns were used. And finally, the last step is the classification system, implemented by Support Vector Machines.","PeriodicalId":158982,"journal":{"name":"2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automatic age detection based on facial images\",\"authors\":\"Verónica Almeida, M. Dutta, C. Travieso, Anushikha Singh, J. B. Alonso\",\"doi\":\"10.1109/CCINTELS.2016.7878211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the proposed implementation of a soft-biometric system for automatic age detection from facial images is described. In order to do this, the method followed was that of a classical biometric system. The first step is preprocessing, to enhance the feature extraction. The next step is the parameterization, where techniques like wavelet transformed, discrete cosine transformed or local binary patterns were used. And finally, the last step is the classification system, implemented by Support Vector Machines.\",\"PeriodicalId\":158982,\"journal\":{\"name\":\"2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCINTELS.2016.7878211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 2nd International Conference on Communication Control and Intelligent Systems (CCIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCINTELS.2016.7878211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, the proposed implementation of a soft-biometric system for automatic age detection from facial images is described. In order to do this, the method followed was that of a classical biometric system. The first step is preprocessing, to enhance the feature extraction. The next step is the parameterization, where techniques like wavelet transformed, discrete cosine transformed or local binary patterns were used. And finally, the last step is the classification system, implemented by Support Vector Machines.