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International Journal of Computer Vision

Erschienen in:

10.08.2016

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

verfasst von: Rasmus Rothe, Radu Timofte, Luc Van Gool

Erschienen in: International Journal of Computer Vision | Ausgabe 2-4/2018

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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.

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Titel: Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
verfasst von: Rasmus Rothe
Radu Timofte
Luc Van Gool
Publikationsdatum: 10.08.2016
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2-4/2018
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-016-0940-3

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