{"title":"学习基于分布的人脸模型用于人脸检测","authors":"K. Sung, S. Poggio","doi":"10.1109/NNSP.1995.514914","DOIUrl":null,"url":null,"abstract":"We present a distribution-based modeling cum example-based learning approach for detecting human faces in cluttered scenes. The distribution-based model captures complex variations in human face patterns that cannot be adequately described by classical pictorial template-based matching techniques or geometric model-based pattern recognition schemes. We also show how explicitly modeling the distribution of certain \"facelike\" nonface patterns can help improve classification results.","PeriodicalId":403144,"journal":{"name":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Learning a distribution-based face model for human face detection\",\"authors\":\"K. Sung, S. Poggio\",\"doi\":\"10.1109/NNSP.1995.514914\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a distribution-based modeling cum example-based learning approach for detecting human faces in cluttered scenes. The distribution-based model captures complex variations in human face patterns that cannot be adequately described by classical pictorial template-based matching techniques or geometric model-based pattern recognition schemes. We also show how explicitly modeling the distribution of certain \\\"facelike\\\" nonface patterns can help improve classification results.\",\"PeriodicalId\":403144,\"journal\":{\"name\":\"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing\",\"volume\":\"77 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.1995.514914\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1995.514914","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning a distribution-based face model for human face detection
We present a distribution-based modeling cum example-based learning approach for detecting human faces in cluttered scenes. The distribution-based model captures complex variations in human face patterns that cannot be adequately described by classical pictorial template-based matching techniques or geometric model-based pattern recognition schemes. We also show how explicitly modeling the distribution of certain "facelike" nonface patterns can help improve classification results.
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