S. A. Nazeer, Nazaruddin Omar, Khairol Faisal Jumari, M. Khalid
{"title":"基于人工神经网络的人脸检测方法","authors":"S. A. Nazeer, Nazaruddin Omar, Khairol Faisal Jumari, M. Khalid","doi":"10.1109/AMS.2007.38","DOIUrl":null,"url":null,"abstract":"A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the AdaBoost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance","PeriodicalId":198751,"journal":{"name":"First Asia International Conference on Modelling & Simulation (AMS'07)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Face Detecting Using Artificial Neural Network Approach\",\"authors\":\"S. A. Nazeer, Nazaruddin Omar, Khairol Faisal Jumari, M. Khalid\",\"doi\":\"10.1109/AMS.2007.38\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the AdaBoost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance\",\"PeriodicalId\":198751,\"journal\":{\"name\":\"First Asia International Conference on Modelling & Simulation (AMS'07)\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-03-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First Asia International Conference on Modelling & Simulation (AMS'07)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AMS.2007.38\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First Asia International Conference on Modelling & Simulation (AMS'07)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AMS.2007.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Face Detecting Using Artificial Neural Network Approach
A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the AdaBoost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance