{"title":"Gender face Recognition Using Advanced Convolutional Neural Network Model","authors":"Hafsa Yousif, Isselmou Abd El Kader","doi":"10.1109/dsins54396.2021.9670602","DOIUrl":null,"url":null,"abstract":"Gender face recognition has several useful applications in human Android reactions, in which the overall user experience can improved. The convolutional neural network model has made excellent achievements in this field. This paper proposed an advanced convolutional neural network model named \"A-CNN\" to recognize gender. The advantage of the model is training big data without technical challenges and achieved excellent overall performance. The model train used 5000 images, which consisted of gender face male and female. The results have shown that the proposed model gives excellent achievement during the training stage with an accuracy of 97% and loss validation 0.1. The result has demonstrated that the proposed model can facilitate the automatic classification of human gender.","PeriodicalId":243724,"journal":{"name":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Digital Society and Intelligent Systems (DSInS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/dsins54396.2021.9670602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Gender face recognition has several useful applications in human Android reactions, in which the overall user experience can improved. The convolutional neural network model has made excellent achievements in this field. This paper proposed an advanced convolutional neural network model named "A-CNN" to recognize gender. The advantage of the model is training big data without technical challenges and achieved excellent overall performance. The model train used 5000 images, which consisted of gender face male and female. The results have shown that the proposed model gives excellent achievement during the training stage with an accuracy of 97% and loss validation 0.1. The result has demonstrated that the proposed model can facilitate the automatic classification of human gender.