Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
Open access
Date
2018-04Type
- Journal Article
Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-a-010811104Publication status
publishedExternal links
Journal / series
International Journal of Computer VisionVolume
Pages / Article No.
Publisher
SpringerSubject
Age estimation; Deep learning; CNN; RegressionOrganisational unit
03514 - Van Gool, Luc / Van Gool, Luc
Notes
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.More
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