{"title":"手机环境下的鲁棒唇中心检测","authors":"T. Pham, M. Song, J. Y. Kim, S. Na, S. Hwang","doi":"10.1109/ISSPIT.2008.4775724","DOIUrl":null,"url":null,"abstract":"In this paper we present a new approach for the detection of lip centers based on eye localization that is adopted into a lip reading system in mobile environments. First, the centers of left eyes and right eyes are localized directly. Then we use the geometry characteristics of faces to extract rough lip regions. Next, we use 2 steps of threshold adaptation to binarize lip images. The first threshold adaptation is used to estimate standard lip threshold for each image; and the second one is applied to compute thresholds for left and right lip image based on standard lip threshold. Finally we apply Sobel edge map based filter with projections to detect precise lip centers. Experimental study shows that our algorithm can work well under various illumination conditions that is one of the typical difficulties of image processing and computer vision problems.","PeriodicalId":213756,"journal":{"name":"2008 IEEE International Symposium on Signal Processing and Information Technology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Robust Lip Center Detection in Cell Phone Environment\",\"authors\":\"T. Pham, M. Song, J. Y. Kim, S. Na, S. Hwang\",\"doi\":\"10.1109/ISSPIT.2008.4775724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a new approach for the detection of lip centers based on eye localization that is adopted into a lip reading system in mobile environments. First, the centers of left eyes and right eyes are localized directly. Then we use the geometry characteristics of faces to extract rough lip regions. Next, we use 2 steps of threshold adaptation to binarize lip images. The first threshold adaptation is used to estimate standard lip threshold for each image; and the second one is applied to compute thresholds for left and right lip image based on standard lip threshold. Finally we apply Sobel edge map based filter with projections to detect precise lip centers. Experimental study shows that our algorithm can work well under various illumination conditions that is one of the typical difficulties of image processing and computer vision problems.\",\"PeriodicalId\":213756,\"journal\":{\"name\":\"2008 IEEE International Symposium on Signal Processing and Information Technology\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE International Symposium on Signal Processing and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSPIT.2008.4775724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Symposium on Signal Processing and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPIT.2008.4775724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Robust Lip Center Detection in Cell Phone Environment
In this paper we present a new approach for the detection of lip centers based on eye localization that is adopted into a lip reading system in mobile environments. First, the centers of left eyes and right eyes are localized directly. Then we use the geometry characteristics of faces to extract rough lip regions. Next, we use 2 steps of threshold adaptation to binarize lip images. The first threshold adaptation is used to estimate standard lip threshold for each image; and the second one is applied to compute thresholds for left and right lip image based on standard lip threshold. Finally we apply Sobel edge map based filter with projections to detect precise lip centers. Experimental study shows that our algorithm can work well under various illumination conditions that is one of the typical difficulties of image processing and computer vision problems.
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