{"title":"基于反向传播的二维对称Gabor特征人脸识别模型","authors":"B. Vinay Kumar, B. Shreyas, C.N.S. Ganesh Murthy","doi":"10.1109/ICSCN.2007.350776","DOIUrl":null,"url":null,"abstract":"We present a system for recognizing human faces from a database consisting of multiple images per test subject, which spans the normal variations in a human face. The faces are represented based on a Gabor wavelet transform. The features are extracted as a vector of values using a carefully chosen symmetrical Gabor wavelet matrix. This feature extraction is biologically motivated and models systems based on human vision. The extracted features are fed into an artificial neural network, in dual phases. The training and testing phases of the neural network work on the features extracted by the same method. Excellent pattern-recognition-specific neural network like a multilayer perceptron with back propagation provides the necessary classification once the feature extraction is complete","PeriodicalId":257948,"journal":{"name":"2007 International Conference on Signal Processing, Communications and Networking","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Back Propagation Based Face Recognition Model, using 2D Symmetrical Gabor Features\",\"authors\":\"B. Vinay Kumar, B. Shreyas, C.N.S. Ganesh Murthy\",\"doi\":\"10.1109/ICSCN.2007.350776\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a system for recognizing human faces from a database consisting of multiple images per test subject, which spans the normal variations in a human face. The faces are represented based on a Gabor wavelet transform. The features are extracted as a vector of values using a carefully chosen symmetrical Gabor wavelet matrix. This feature extraction is biologically motivated and models systems based on human vision. The extracted features are fed into an artificial neural network, in dual phases. The training and testing phases of the neural network work on the features extracted by the same method. Excellent pattern-recognition-specific neural network like a multilayer perceptron with back propagation provides the necessary classification once the feature extraction is complete\",\"PeriodicalId\":257948,\"journal\":{\"name\":\"2007 International Conference on Signal Processing, Communications and Networking\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 International Conference on Signal Processing, Communications and Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSCN.2007.350776\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 International Conference on Signal Processing, Communications and Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSCN.2007.350776","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Back Propagation Based Face Recognition Model, using 2D Symmetrical Gabor Features
We present a system for recognizing human faces from a database consisting of multiple images per test subject, which spans the normal variations in a human face. The faces are represented based on a Gabor wavelet transform. The features are extracted as a vector of values using a carefully chosen symmetrical Gabor wavelet matrix. This feature extraction is biologically motivated and models systems based on human vision. The extracted features are fed into an artificial neural network, in dual phases. The training and testing phases of the neural network work on the features extracted by the same method. Excellent pattern-recognition-specific neural network like a multilayer perceptron with back propagation provides the necessary classification once the feature extraction is complete