使用迁移学习和K-fold交叉验证的面部图像年龄估计

S. M. S. Uddin, Md. Samin Morshed, Mahruf Islam Prottoy, A. Rahman
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引用次数: 4

摘要

自动年龄估计由于具有广泛的应用前景,近年来受到越来越多的关注。大多数技术使用手工制作的特征来预测老化模式,但不够准确,无法有效使用。卷积神经网络(CNN)深度学习特征提取的最新进展允许设计更准确的面部分析。本文的目的是探索使用深度学习方法的不同年龄估计技术的性能,并提出迁移学习的一种变体,该变体在迁移学习的基础上使用K-fold交叉验证。实验采用UTKFace数据集,采用VGG16、ResNet50和SENet50模型。结果表明,我们的方法在性能上优于现有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Age Estimation from Facial Images using Transfer Learning and K-fold Cross-Validation
Automatic Age estimation has gained more and more interest in recent years due to its potential in many applications. Most techniques uses hand-crafted features to predict aging patterns, but not accurate enough to be employed effectively. Recent advances in deeply learned features extracted by Convolutional Neural Network (CNN) allows to design more accurate facial analysis. The aim of this paper is to explore the performance of different age estimation techniques that uses Deep Learning methods and to propose a variation of Transfer learning which uses K-fold cross validation on top of transfer learning. The experiment was carried out with UTKFace dataset using VGG16, ResNet50 and SENet50 models. The result demonstrates that our method is Superior to the existing methods in terms of performance.
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