{"title":"一个实时点头和摇头探测器","authors":"Ashish Kapoor, Rosalind W. Picard","doi":"10.1145/971478.971509","DOIUrl":null,"url":null,"abstract":"Head nods and head shakes are non-verbal gestures used often to communicate intent, emotion and to perform conversational functions. We describe a vision-based system that detects head nods and head shakes in real time and can act as a useful and basic interface to a machine. We use an infrared sensitive camera equipped with infrared LEDs to track pupils. The directions of head movements, determined using the position of pupils, are used as observations by a discrete Hidden Markov Model (HMM) based pattern analyzer to detect when a head nod/shake occurs. The system is trained and tested on natural data from ten users gathered in the presence of varied lighting and varied facial expressions. The system as described achieves a real time recognition accuracy of 78.46% on the test dataset.","PeriodicalId":416822,"journal":{"name":"Workshop on Perceptive User Interfaces","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"174","resultStr":"{\"title\":\"A real-time head nod and shake detector\",\"authors\":\"Ashish Kapoor, Rosalind W. Picard\",\"doi\":\"10.1145/971478.971509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Head nods and head shakes are non-verbal gestures used often to communicate intent, emotion and to perform conversational functions. We describe a vision-based system that detects head nods and head shakes in real time and can act as a useful and basic interface to a machine. We use an infrared sensitive camera equipped with infrared LEDs to track pupils. The directions of head movements, determined using the position of pupils, are used as observations by a discrete Hidden Markov Model (HMM) based pattern analyzer to detect when a head nod/shake occurs. The system is trained and tested on natural data from ten users gathered in the presence of varied lighting and varied facial expressions. The system as described achieves a real time recognition accuracy of 78.46% on the test dataset.\",\"PeriodicalId\":416822,\"journal\":{\"name\":\"Workshop on Perceptive User Interfaces\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"174\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Perceptive User Interfaces\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/971478.971509\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Perceptive User Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/971478.971509","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Head nods and head shakes are non-verbal gestures used often to communicate intent, emotion and to perform conversational functions. We describe a vision-based system that detects head nods and head shakes in real time and can act as a useful and basic interface to a machine. We use an infrared sensitive camera equipped with infrared LEDs to track pupils. The directions of head movements, determined using the position of pupils, are used as observations by a discrete Hidden Markov Model (HMM) based pattern analyzer to detect when a head nod/shake occurs. The system is trained and tested on natural data from ten users gathered in the presence of varied lighting and varied facial expressions. The system as described achieves a real time recognition accuracy of 78.46% on the test dataset.