计算机工程与应用2019，Vol.55Issue(18)：173-179,188,8.DOI:10.3778/j.issn.1002-8331.1805-0455
改进的VGG网络可提升年龄与性别预测准确率
Improved VGG-Net for Increasing Precision of Age and Gender Prediction
摘要
Abstract
Since deep Convolutional Neural Network(CNN)has good properties of feature learning, it has been investi-gated deeply and applied widely. Compared with the amazing results applied by deep CNN in object recognition and clas-sification, its application in age prediction and gender discrimination is far away from practice. This paper designs a deep CNN and trains the model on identification photos and Adience dataset, so as to apply it in the prediction of people’s age and gender classification. Experiments based on Tensorflow show that this model is able to successfully estimate individuals’ age at an accuracy rate about 90% and gender at 93% respectively. The results are better than others.关键词
深度卷积神经网络/年龄预测/性别分类Key words
deep Convolutional Neural Network(CNN)/age prediction/gender classification分类
信息技术与安全科学引用本文复制引用
周玉阳,秦科..改进的VGG网络可提升年龄与性别预测准确率[J].计算机工程与应用,2019,55(18):173-179,188,8.基金项目
四川省科技厅应用基础重点(No.2017JY0073) （No.2017JY0073）
2016年电子科技大学"一校一带"行动计划创新创业开放基金(No. A03013023001035) （No. A03013023001035）
中央高校基本科研业务费(No.ZYGX2016J083). （No.ZYGX2016J083）
