In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.

中文翻译：

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从没有面部标志的单一图像中对真实和表观年龄的深度期望

在本文中，我们提出了一种深度学习解决方案，用于在不使用面部标志的情况下从单个人脸图像中估计年龄，并介绍了 IMDB-WIKI 数据集，这是最大的带有年龄和性别标签的人脸图像公共数据集。如果实际年龄估计研究跨越数十年，那么对表观年龄估计或其他人从面部图像中感知到的年龄的研究是最近的一项努力。我们使用 VGG-16 架构的卷积神经网络 (CNN) 处理这两项任务，这些网络在 ImageNet 上进行了预训练以进行图像分类。我们将年龄估计问题作为一个深度分类问题，然后是 softmax 期望值细化。我们解决方案的关键因素是：从大数据中深度学习的模型、强大的人脸对齐以及年龄回归的期望值公式。