Gender face Recognition Using Advanced Convolutional Neural Network Model

Hafsa Yousif, Isselmou Abd El Kader
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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.
基于高级卷积神经网络模型的人脸性别识别
性别面部识别在人类安卓反应中有几个有用的应用,可以改善整体用户体验。卷积神经网络模型在这一领域取得了优异的成绩。本文提出了一种先进的卷积神经网络模型“A-CNN”来识别性别。该模型的优势在于训练大数据时没有技术挑战,整体表现优异。火车模型使用了5000张图像,包括性别脸,男性和女性。结果表明,该模型在训练阶段取得了优异的成绩,准确率为97%,损失验证率为0.1。结果表明,该模型能够实现人类性别的自动分类。
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