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2018 | OriginalPaper | Buchkapitel

Deep Learning Features for Face Age Estimation: Better Than Human?

verfasst von : Krzysztof Kotowski, Katarzyna Stapor

Erschienen in: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety

Verlag: Springer International Publishing

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Abstract

Deep convolutional neural networks have the ability to infer highly representative features from data. We decided to use this power for the purpose of estimating the human face age from a single colour image. We trained the Support Vector Machine regression model on raw feature vectors from the FaceNet deep neural network pretrained for face recognition. Our proposed method is a simple but effective FaceNet extension which does not need large scale data. In order to measure the accuracy of our approach, we proposed a test procedure on the FACES database for which we achieved the mean absolute error of 5.18 and the mean error of 0.09 years. Then, we conducted an experiment employing 78 students and showed that our method outperforms human for faces in the regular upright orientation. For vertically inverted faces, we reported an age underestimation trend in responses of students and our method.

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Vorheriges Kapitel B4MultiSR: A Benchmark for Multiple-Image Super-Resolution Reconstruction

Nächstes Kapitel The Use of Minimal Geometries in Automated Building Generalization

Nur mit Berechtigung zugänglich

http://www-prima.inrialpes.fr/FGnet/html/benchmarks.html.

Many thanks to Mr David Sandberg from github.com/davidsandberg.

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Titel: Deep Learning Features for Face Age Estimation: Better Than Human?
verfasst von: Krzysztof Kotowski
Katarzyna Stapor
Verlag: Springer International Publishing
Buch: Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety
Print ISBN: 978-3-319-99986-9

Electronic ISBN: 978-3-319-99987-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99987-6_29

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