A Lightweight Deep Convolutional Neural Network Model for Real-Time Age and Gender Prediction

Md. Nahidul Islam Opu, Tanha Kabir Koly, Annesha Das, Ashim Dey
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引用次数: 7

Abstract

Recognition of age and gender has become a significant part of the biometric system, protection, and treatment. It is widely used for people to access age-related content. It is used by social media in the distribution of layered advertising and promotions to expand its scope. Application of face detection has grown to a great extent that we should upgrade it using various methods to achieve more accurate results. In this paper, we have developed a lightweight deep Convolution neural network model for real-time age and gender prediction. For making the training dataset more diverse, Wiki, UTKFace, and Adience datasets have been merged into one containing 18728 images. Using this vast mixed dataset, we have achieved accuracy of 48.59% and 80.76% for age and gender respectively. Further, the model is tested in real-time. Different experimental investigations on the prepared dataset show that with most recent approaches, our model provides competitive prediction accuracy.
一种用于实时年龄和性别预测的轻量级深度卷积神经网络模型
年龄和性别的识别已成为生物识别系统、保护和治疗的重要组成部分。它被广泛用于人们访问与年龄有关的内容。它被社交媒体用于分发分层广告和促销活动,以扩大其范围。人脸检测的应用已经发展到很大程度,我们需要使用各种方法对其进行升级,以获得更准确的结果。在本文中,我们开发了一个轻量级的深度卷积神经网络模型,用于实时预测年龄和性别。为了使训练数据集更加多样化,Wiki、UTKFace和Adience数据集被合并为一个包含18728张图像的数据集。使用这个庞大的混合数据集,我们对年龄和性别的准确率分别达到了48.59%和80.76%。并对模型进行了实时测试。对准备好的数据集进行的不同实验研究表明,使用最新的方法,我们的模型提供了具有竞争力的预测精度。
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