Tsuyoshi Takahashi, Bo Wu, Y. Kageyama, M. Nishida, M. Ishii
{"title":"连续热图像中人脸区域自动检测的学习数据量研究","authors":"Tsuyoshi Takahashi, Bo Wu, Y. Kageyama, M. Nishida, M. Ishii","doi":"10.1109/GCCE.2015.7398530","DOIUrl":null,"url":null,"abstract":"Chronological change of temperature on cheeks includes important information to detect an emotion occurrence. To measure the specific region of face skin temperature accurately, we have developed a face detection method from sequential thermal image acquired in 30fps. In this paper, we investigated minimum quantity of learning data that is sufficient to create a high accurate face area detector. The experimental results for five persons showed that high detection rate was obtained when using over 350 images.","PeriodicalId":363743,"journal":{"name":"2015 IEEE 4th Global Conference on Consumer Electronics (GCCE)","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A study of learning data size for automatic face area detection in sequential thermal images\",\"authors\":\"Tsuyoshi Takahashi, Bo Wu, Y. Kageyama, M. Nishida, M. Ishii\",\"doi\":\"10.1109/GCCE.2015.7398530\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Chronological change of temperature on cheeks includes important information to detect an emotion occurrence. To measure the specific region of face skin temperature accurately, we have developed a face detection method from sequential thermal image acquired in 30fps. In this paper, we investigated minimum quantity of learning data that is sufficient to create a high accurate face area detector. The experimental results for five persons showed that high detection rate was obtained when using over 350 images.\",\"PeriodicalId\":363743,\"journal\":{\"name\":\"2015 IEEE 4th Global Conference on Consumer Electronics (GCCE)\",\"volume\":\"2017 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 4th Global Conference on Consumer Electronics (GCCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GCCE.2015.7398530\",\"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 IEEE 4th Global Conference on Consumer Electronics (GCCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GCCE.2015.7398530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A study of learning data size for automatic face area detection in sequential thermal images
Chronological change of temperature on cheeks includes important information to detect an emotion occurrence. To measure the specific region of face skin temperature accurately, we have developed a face detection method from sequential thermal image acquired in 30fps. In this paper, we investigated minimum quantity of learning data that is sufficient to create a high accurate face area detector. The experimental results for five persons showed that high detection rate was obtained when using over 350 images.